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Original article
Utility of PET/CT in the diagnosis and staging of lung cancer after ecobronchoscopy in mining population
Utilidad de la PET/TC en el diagnóstico y estadificación del cáncer de pulmón tras ecobroncoscopia en población minera
Marco López Zubizarretaa,
Corresponding author
marlocopez@hotmail.com

Corresponding author.
, Rosa Cordovilla Pérezb, Aldo Mateo Torracchic, Virginia Guevara Velázqueza
a Sección de Neumología, Complejo Asistencial de Ávila, Ávila, Spain
b Servicio de Neumología, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain
c Servicio de Neumología, Universidad de Azuay, Cuenca, Ecuador
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Bronchogenic carcinoma &#40;BC&#41; is one of the diseases with the highest mortality worldwide and&#44; undoubtedly&#44; the most lethal one in the respiratory sphere&#46; Non-small-cell lung carcinoma &#40;NSCLC&#41; represents 80&#37; of all cases of BC&#44; and its five-year survival rate is of approximately 15&#37;&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> In 2017&#44; tracheal&#44; bronchial&#44; and lung cancer had a global incidence of approximately 2&#46;2 million cases &#40;affecting 1&#44;468&#44;000 men and 695&#44;000 women&#41; and caused 1&#46;9 million deaths worldwide&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Mining is&#44; and continues to be&#44; an activity of enormous importance in the economic growth of Spain&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> However&#44; its negative impact on general health and&#44; especially&#44; in the field of respiratory diseases&#44; was widely demonstrated and documented in countless studies carried out in the 1970s and 1980s&#44; in which its relationship with silicosis&#44; tuberculosis&#44; and chronic obstructive pulmonary disease &#40;COPD&#41;&#44; among others&#44; was made clear&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;5</span></a> Although the interaction between mining &#40;silica exposure&#41; and lung cancer has been a controversial issue for decades&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> a positive association between occupational silica exposure and lung cancer has recently been demonstrated&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In recent decades there have also been important changes in the field of lung cancer diagnosis and staging&#46; The most important advances achieved in this field can be summarized in the following points&#58; &#40;1&#41; improvements in imaging techniques&#44; including the use of multislice computed tomography &#40;CT&#41; with a higher image resolution capable of detecting smaller nodules<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a>&#59; &#40;2&#41; the development of diagnostic and staging techniques with a minimally invasive approach&#44; especially aspiration puncture guided by endoscopic ultrasound &#40;EUS&#41; and endobronchial ultrasound &#40;EBUS&#41;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;12</span></a>&#59; and&#44; &#40;3&#41; the use of positron emission tomography &#40;PET&#41; and PET combined with computed tomography &#40;PET&#47;CT&#41;&#44; which have generally been considered positive for malignancy when the standardized uptake value &#40;SUV&#41; is greater than 2&#46;5 &#40;SUV<span class="elsevierStyleInf">max</span>&#8239;&#62;&#8239;2&#46;5&#41;&#44;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#44;14</span></a> although other values&#44; such as the mean SUV&#44; the dual-time SUV<span class="elsevierStyleInf">max</span>&#44; or the tumor SUV&#47;adenopathy SUV ratio&#44; can also be used&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> As is the case with any diagnostic test&#44; its accuracy is determined by the balance between false positives and false negatives&#46; False positives can be explained by increased fluorodeoxyglucose &#40;FDG&#41; uptake due to the increased glycolysis in leukocytes&#44; lymphocytes&#44; and macrophages&#44; as in the case of infections or inflammatory processes&#44; such as pneumonias&#44; caseating granulomas&#44; sarcoidosis&#44; amyloidosis&#44; pleurodesis&#44; round atelectasis&#44; etc&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">In the case of the population with prolonged exposure to inflammatory agents&#44; such as miners&#44; the likelihood of false positives in PET&#47;CT scans is higher&#44; as demonstrated in previous studies performed on subjects with a history of coal exposure and exhibiting hypermetabolic pulmonary nodules in PET&#47;CT scans&#44; but whose final histology results were negative for malignancy&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Our work will focus on analyzing the SUV<span class="elsevierStyleInf">max</span> as a determinant of malignancy in populations both with and without a history of mining exposure&#46; These PET&#47;CT data have been decisive for the indication of invasive EBUS staging&#44; as well as for the selection of adenopathies during the diagnostic procedure carried out with the aid of this test&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">We hypothesize that the mining population should not have the same SUV<span class="elsevierStyleInf">max</span> limits as the general population&#44; and that a higher SUV<span class="elsevierStyleInf">max</span> would probably be necessary to suggest the presence of malignancy in this population&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">Considering a potentially higher likelihood of error of PET&#47;CT in terms of lung cancer diagnosis in the mining population&#44; the aim of our work focuses on determining whether an SUV<span class="elsevierStyleInf">max</span>&#8239;&#62;&#8239;2&#46;5 in mediastinal adenopathies can be considered an independent risk factor for malignancy in both the population with a history of mining exposure and that without it&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">As for secondary objectives&#44; we also analyzed other factors such as age&#44; adenopathy size greater than 1&#8239;cm&#44; and history of smoking&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Patients and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Study design</span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Population</span><p id="par0045" class="elsevierStylePara elsevierViewall">Between May 2014 and December 2019&#44; we retrospectively selected all patients who had undergone an EBUS for lung cancer diagnosis and&#47;or staging and had previously undergone a PET&#47;CT scan with <span class="elsevierStyleSup">18</span>F-FDG from a prospective database&#46; In the case of patients who had undergone an EBUS for staging purposes&#44; this procedure had been carried out systematically and&#44; therefore&#44; the PET values were only used as an orientation&#46; However&#44; in those patients who had undergone an EBUS for lung cancer diagnosis purposes&#44; the PET scan findings were used as indicators of the areas to be punctured&#46; The PET&#47;CT scan was always performed prior to the EBUS&#44; and the maximum time elapsed between both tests was 15 days&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Because of its location&#44; the Salamanca Healthcare Complex &#40;<span class="elsevierStyleItalic">Complejo Asistencial de Salamanca</span>&#41; attends patients from a wide geographical region of the province of Salamanca &#40;Spain&#41;&#44; but also from the northern part of the country&#44; specifically from the town of Ponferrada and its surrounding area&#44; whose population is characterized by exposure to mining&#44; particularly of silica and coal&#46; For this reason&#44; we divided the patients into two groups&#58; a first group &#40;group 1&#41; including patients who had not been exposed to mining&#44; and a second one &#40;group 2&#41; formed by patients who had been exposed to this activity&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">The variables analyzed in both groups were sex&#44; age&#44; smoking history&#44; occupational exposure to mining&#44; adenopathy size measured during an EBUS&#44; PET&#47;CT SUV&#44; and pathology exam results&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">A PET&#47;CT scan was considered positive when the SUV<span class="elsevierStyleInf">max</span> was greater than 2&#46;5&#44; as suggested by some authors in the paper published by Pak et al&#46; and according to the indications of our center&#8217;s Nuclear Medicine Department&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">An adenopathy was considered to be suggestive of malignancy &#40;and consequently punctured under EBUS guidance&#41; when its size was greater than 5&#8239;mm or the PET scan was positive&#46; The pathology exam results were classified as positive if the sample contained malignant cells&#44; negative if it contained lymphocytes&#44; and invalid when none of these criteria were met and it was insufficient to reach a diagnosis&#46; The gold-standard for the EBUS was a histopathological analysis of the adenopathy excised through a mediastinoscopy or surgical resection&#46;</p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Statistical analysis</span><p id="par0070" class="elsevierStylePara elsevierViewall">For our statistical model&#44; the observation unit was not the subject&#44; but rather the punctured adenopathy&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Our study sample was divided into two groups based on the subjects&#8217; occupational history&#58; group 1 included adenopathies obtained from subjects without a previous exposure to mining activities&#44; and group 2 included those obtained from subjects who had been exposed to mining&#46; In the binomial logistic regression model&#44; the dependent variable was considered to be the pathology exam result in terms of malignancy &#40;neoplastic cells in the final histopathology report&#41; or non-malignancy &#40;valid sample without neoplastic cells&#41;&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">The independent variables &#40;risk factors for malignancy&#41; were an age older than 65 years&#44; an adenopathy size greater than 1&#8239;cm &#40;measured during the EBUS&#41;&#44; and a PET&#47;CT SUV<span class="elsevierStyleInf">max</span> greater than 2&#46;5&#46; The subjects&#8217; sex was excluded as a variable in the logistic regression models due to the existence of a high collinearity phenomenon &#40;Pearson&#8217;s correlation coefficient of 0&#46;78 and typified residuals of 10&#46;1&#41; with the smoking variable&#46; The smoking variable was also excluded in the model applied on the mining population owing to the fact that all miners were ex-smokers&#46; In the non-mining population&#44; non-smokers were retained because of the importance of this variable and its strengthening of the mathematical model&#46; The statistical analysis was carried out using software SPSS 20&#44; through the license granted to the University of Salamanca&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">The study was reviewed and approved by the Ethics Committee of the Salamanca Healthcare Complex&#46;</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Results</span><p id="par0090" class="elsevierStylePara elsevierViewall">A total of 583 adenopathies punctured from 339 subjects &#40;40 of whom were miners&#41; under EBUS guidance were analyzed&#46; On average&#44; 1&#46;7 adenopathies were punctured per subject&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The number of punctured adenopathies was 514 in the group of subjects without a previous exposure to mining &#40;299 subjects&#41; and 69 in those with a history of exposure to mining &#40;40 miners&#41;&#46; The male sex predominated in both groups &#40;87&#46;2&#37;&#41;&#44; with 100&#37; of the group of exposed subjects being men owing to the occupational characteristics inherent to mining&#46; A history of smoking also predominated in both groups&#44; to such an extent that the entire mining group were either current or ex-smokers&#46; The age of the subjects of both groups was similar&#44; with a mean of 66&#46;2 years&#44; and no statistically significant differences were observed between both groups in the characteristics of the adenopathies in terms of their size and PET&#47;CT SUV&#46; As for the presence of neoplasms&#44; a higher frequency was detected in the unexposed group&#44; with this difference being statistically significant&#46; <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows the characteristics of both groups according to the variables under study and the statistical bivariate comparison&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0100" class="elsevierStylePara elsevierViewall">As shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; the logistic regression model showed that in the population without a history of exposure to mining&#44; an age older than 65 years &#40;odds ratio &#91;OR&#93;&#8239;&#61;&#8239;2&#46;6&#41;&#44; an adenopathy size greater than 1&#8239;cm measured during the EBUS &#40;OR&#8239;&#61;&#8239;7&#46;2&#41;&#44; a PET&#47;CT SUV<span class="elsevierStyleInf">max</span> greater than 2&#46;5 &#40;OR&#8239;&#61;&#8239;5&#46;5&#41;&#44; and a history of smoking &#40;OR&#8239;&#61;&#8239;6&#46;1&#41; behaved as independent factors capable of predicting malignancy in the histopathology study of the sample obtained under EBUS guidance&#46; Whereas in adenopathies punctured from the population exposed to mining&#44; none of the above variables could predict malignancy&#44; i&#46;e&#46;&#44; an age over 65 years &#40;OR&#8239;&#61;&#8239;1&#46;3&#41;&#44; an adenopathy greater than 1&#8239;cm measured during the EBUS &#40;OR&#8239;&#61;&#8239;1&#46;6&#41;&#44; a PET&#47;CT SUV<span class="elsevierStyleInf">max</span> greater than 2&#46;5 &#40;OR&#8239;&#61;&#8239;0&#46;7&#41;&#44; and a history of smoking&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0105" class="elsevierStylePara elsevierViewall">It is important to emphasize that multiple logistic regression models were performed with different adenopathy sizes and SUV<span class="elsevierStyleInf">max</span> cut-off points&#46; Because none of the tested models was powerful enough&#44; we maintained an adenopathy size of 1&#8239;cm and an SUV<span class="elsevierStyleInf">max</span> of 2&#46;5 as a risk factor for malignancy&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">Finally&#44; we set an SUV<span class="elsevierStyleInf">max</span> cut-off point defining the highest likelihood of malignancy in each of the populations under study&#59; that is&#44; 3&#46;7 for the unexposed population &#40;81&#37; sensitivity and 75&#37; specificity&#41; and 4&#46;8 for the mining population &#40;71&#37; sensitivity and 42&#37; specificity&#41;&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Discussion</span><p id="par0115" class="elsevierStylePara elsevierViewall">The accuracy with which we are able to predict mediastinal lymph node malignancy is critical in lung cancer diagnosis&#44; prognosis&#44; and management&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#44;19</span></a> In this context&#44; PET&#47;CT has proven to be useful in distinguishing between benign and malignant adenopathies&#44; with a high negative predictive value that aids in mediastinal staging through EBUS or mediastinoscopy&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><p id="par0120" class="elsevierStylePara elsevierViewall">There is also a positive relationship between the size of the mediastinal adenopathy&#44; as measured by a tomography or EBUS&#44; and its degree of malignancy&#44; such that the larger the size the greater the likelihood of malignancy&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> Studies such as that of Mattes et al&#46; proposed mathematical models capable of predicting the likelihood of malignancy of an adenopathy based on variables such as its size &#40;the larger the lymph node&#8217;s short axis the greater the likelihood of malignancy&#41;&#44; PET&#47;CT SUV&#44; and histological type&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> Although these two criteria determined through imaging tests are widely accepted and based on available scientific literature&#44; there is insufficient evidence to extrapolate these concepts to specific populations&#46; The power of this study is based on several facts&#44; including the reliability of its results considering that it is based on a systematized database meeting quality criteria&#44; that it incorporates a considerably large sample of lymph nodes punctured under EBUS guidance&#44; and that it includes a particular population that is hard to access&#46; In addition&#44; the diagnosis of all negative adenopathies was confirmed through a mediastinoscopy and&#47;or surgery&#46; Positive adenopathies were considered true positives and histological confirmation was not required given the low incidence of false positives yielded by EBUS &#40;2&#37;&#41;&#44; as reported in the literature&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#44;23</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">Therefore&#44; the presented mathematical model can be a useful tool in the decision-making process in patients with a suspected pulmonary neoplasm and no history of occupational exposure to mining&#46; However&#44; it is completely worthless for the exposed population&#44; as none of the analyzed variables is capable of predicting malignancy&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">The first logistic model obtained in the unexposed population is consistent with most models and publications arguing that the likelihood of malignancy of a mediastinal adenopathy in subjects with suspected lung cancer is related to the size of the adenopathy and the PET&#47;CT SUV<span class="elsevierStyleInf">max</span>&#46; That is&#44; the larger the short transverse diameter with an uptake value greater than 2&#46;5&#44; the greater the likelihood of malignancy&#46;<a class="elsevierStyleCrossRefs" href="#bib0090"><span class="elsevierStyleSup">18&#44;19&#44;24&#44;25</span></a> In this first mathematical model&#44; the two variables behave as independent risk factors for malignancy with an OR of 7&#46;2 when the size of the adenopathy is greater than 1&#8239;cm&#44; as measured during an EBUS&#44; and of 5&#46;2 when the PET&#47;CT SUV is greater than 2&#46;5 &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">However&#44; when we applied a similar logistic model&#44; but in a population exposed to mining&#44; the results differed&#44; as none of the independent risk factors for lung cancer described above &#40;adenopathy size and PET&#47;CT SUV&#41; behaved as predictors of malignancy&#46; The diagnostic problems associated with PET&#47;CT have previously been studied in the context of extrathoracic neoplastic diseases&#44; in which case false positives have been detected in patients with inflammatory diseases&#44; granulomatous diseases&#44; chronic pancreatitis&#44; fibrosis&#44; or salivary gland tumors&#44;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> as well as in intrathoracic diseases&#44; such as infections &#40;bacterial&#44; fungal&#44; or tuberculosis&#41;&#44; sarcoidosis&#44; post-radiation pneumonitis&#44; and post-surgical changes&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">In clinical practice there is very little information available about the usefulness of PET&#47;CT in lung cancer diagnosis and staging in patients exposed to mining&#46; In these patients&#44; chronic inflammation due to structural changes in the lymph nodes secondary to coal or silica exposure can trigger an increase in their metabolism with a consequent increase in the adenopathy size and PET&#47;CT SUV&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">As shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#44; the samples obtained from the adenopathies of subjects exposed to mining were anthracotic&#44; which conditions an inflammatory state and a pathological PET&#47;CT SUV leading to an erroneous interpretation in terms of the malignancy diagnosis&#46; Similar results&#44; although from small case series&#44; have previously been published by Reichert et al&#46; and Saydam et al&#46;&#44; who concluded that PET&#47;CT is an imaging technique with limitations in patients with mining exposure&#46;<a class="elsevierStyleCrossRefs" href="#bib0085"><span class="elsevierStyleSup">17&#44;29</span></a></p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0150" class="elsevierStylePara elsevierViewall">A limitation of our study is the use of an SUV<span class="elsevierStyleInf">max</span> cut-off point of 2&#46;5 to predict malignancy as the only PET&#47;CT value&#46; Other authors&#44; such as Shimizu et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a> use the dual-time SUV<span class="elsevierStyleInf">max</span>&#44; although mainly for studying the tumor lesion rather than for evaluating all potentially affected lymph node structures&#46; This is based on the idea that a significant uptake is maintained for several hours in malignant lesions&#44; whereas the uptake decreases markedly over time in benign lesions&#46; However&#44; we were unable to determine the dual-time SUV<span class="elsevierStyleInf">max</span> in our study because a delayed image that would allow such comparison was not obtained&#46;</p><p id="par0155" class="elsevierStylePara elsevierViewall">There are currently authors who use the metabolic tumor volume &#40;MTV&#41; and total lesion glycolysis &#40;TLG&#41;&#44; as these values have been proposed for risk stratification in lung cancer patients&#46; In fact&#44; in a study carried out by Pellegrino et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a> these volumetric imaging values were valid for determining the clinical evolution of these subjects&#44; even allowing for predicting the disease-free period&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">In conclusion&#44; while in the population without a history of mining exposure an adenopathy size greater than 1&#8239;cm and a PET&#47;CT SUV<span class="elsevierStyleInf">max</span> greater than 2&#46;5 can be considered independent risk factors for malignancy&#44; in the population with a history of mining exposure there is no valid predictive model based on complementary imaging techniques that meets these conditions&#46; This is explained by the existence of a greater number of false positives in these subjects compared with the general unexposed population&#44; which limits the usefulness of these techniques in the diagnosis of lung cancer and further supports the need to confirm these findings by cytohistological testing&#46;</p><p id="par0165" class="elsevierStylePara elsevierViewall">It would be advisable to perform prospective studies on this population with a view to adjust the validity of PET&#47;CT in the decision-making process in lung cancer diagnosis&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Conflicts of interest</span><p id="par0170" class="elsevierStylePara elsevierViewall">The authors of this paper declare no conflicts of interest&#46;</p></span></span>"
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            1 => "Tomograf&#237;a por emisi&#243;n de positrones"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Positron emission tomography &#40;PET&#41; with computerized axial tomography &#40;CT&#41; in a single device is known as PET&#47;CT&#46; It has been widely documented and validated&#44; and it is currently a core part of the diagnosis and staging of lung cancer&#46; However&#44; its reliability has not been analysed in specific populations&#46; The objective of this study is to determine the usefulness of PET&#47;CT in patients exposed to mining activities in which an endobronchial ultrasound &#40;EBUS&#41; has been performed for the diagnosis and&#47;or staging of lung cancer&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Patients and methods</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">With a prospective and real-time database&#44; all the patients who had undergone an EBUS with suspicion of lung cancer and who had previously undergone a PET&#47;CT were selected&#46; The observation unit was the lymph node and&#44; based on their history of exposure to mining activities&#44; the sample was divided into two categories&#44; group 1&#58; not exposed&#59; and group 2&#58; exposed&#46; In each group&#44; and with the results from anatomical pathology as a dependent variable&#44; logistic models were established to look for independent risk factors for malignancy&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">In group 1&#44; lymph nodes larger than 1&#8239;cm and PET&#47;CT uptake with maximum standardized uptake value &#40;SUV<span class="elsevierStyleInf">max</span>&#41; over 2&#46;5 were independent risk factors for malignancy&#46; However&#44; in group 2 &#40;exposed patients&#41;&#44; none of those factors were predictors for malignancy&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Discussion</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">In the population of individuals with occupational exposure to mining&#44; PET&#47;CT is an imaging technique with diagnostic limitations for lung cancer&#46;</p></span>"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducci&#243;n</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">La tomograf&#237;a por emisi&#243;n de positrones &#40;PET&#41; en un &#250;nico equipo junto a la tomograf&#237;a axial computarizada &#40;TC&#41; es com&#250;nmente denominada PET&#47;TC&#46; Ha sido ampliamente documentada y validada&#44; de tal manera que hoy d&#237;a forma parte del eje central en el diagn&#243;stico y la estadificaci&#243;n del c&#225;ncer de pulm&#243;n&#46; Sin embargo&#44; su fiabilidad no ha sido estudiada en poblaciones espec&#237;ficas&#46; El objetivo de esta investigaci&#243;n se centra en determinar la utilidad de la PET&#47;TC en pacientes expuestos a la miner&#237;a en los que se ha realizado una ecobroncoscopia &#40;EBUS&#41; para diagn&#243;stico y&#47;o estadificaci&#243;n de c&#225;ncer de pulm&#243;n&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Pacientes y m&#233;todos</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Sobre una base de datos prospectiva y en tiempo real&#44; se seleccionaron todos los individuos sometidos a EBUS por sospecha de c&#225;ncer de pulm&#243;n y que previamente se hab&#237;an realizado una PET&#47;TC&#46; La unidad de observaci&#243;n fue la adenopat&#237;a y&#44; en funci&#243;n del antecedente de exposici&#243;n a la miner&#237;a&#44; se dividi&#243; la muestra en dos grupos&#44; grupo 1&#58; no expuestos&#44; grupo 2&#58; expuestos&#46; En cada grupo y tomando como variable dependiente los resultados de anatom&#237;a patol&#243;gica&#44; se realizaron modelos log&#237;sticos en busca de factores de riesgo independientes de malignidad&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">En el grupo 1&#44; el tama&#241;o mayor a 1&#8239;cm de las adenopat&#237;as y la captaci&#243;n en la PET&#47;TC con valor de captaci&#243;n est&#225;ndar m&#225;ximo &#40;SUV<span class="elsevierStyleInf">m&#225;x</span>&#41; superior a 2&#44;5&#44; son factores de riesgo independientes de malignidad&#46; Sin embargo&#44; en el grupo 2 &#40;paciente con exposici&#243;n&#41; ninguno de los factores anteriormente mencionados predice malignidad&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Discusi&#243;n</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">En la poblaci&#243;n de individuos con exposici&#243;n laboral a la miner&#237;a&#44; la PET&#47;TC es una t&#233;cnica de imagen con limitaciones diagn&#243;sticas en el c&#225;ncer de pulm&#243;n&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Introducci&#243;n"
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          1 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Pacientes y m&#233;todos"
          ]
          2 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Resultados"
          ]
          3 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Discusi&#243;n"
          ]
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    ]
    "NotaPie" => array:1 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as&#58; L&#243;pez Zubizarreta M&#44; Cordovilla P&#233;rez R&#44; Mateo Torracchi A&#44; Guevara Vel&#225;zquez V&#46; Utilidad de la PET&#47;TC en el diagn&#243;stico y estadificaci&#243;n del c&#225;ncer de pulm&#243;n tras ecobroncoscopia en poblaci&#243;n minera&#46; Med Clin &#40;Barc&#41;&#46; 2022&#59;158&#58;65&#8211;69&#46;</p>"
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    ]
    "multimedia" => array:3 [
      0 => array:8 [
        "identificador" => "fig0005"
        "etiqueta" => "Fig&#46; 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 1583
            "Ancho" => 1583
            "Tamanyo" => 148732
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0005"
            "detalle" => "Fig&#46; "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Anthracotic sample obtained under EBUS guidance&#46;</p>"
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      ]
      1 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0010"
            "detalle" => "Table "
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        "tabla" => array:2 [
          "leyenda" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">EBUS&#58; endobronchial ultrasound&#46; PET&#47;CT&#58; positron emission tomography with fused computed tomography images&#46; SD&#58; standard deviation&#46; SUV<span class="elsevierStyleInf">max</span>&#58; maximum standardized uptake value&#46;</p>"
          "tablatextoimagen" => array:1 [
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Group 1 Non-miners &#40;n&#8239;&#61;&#8239;299&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Group 2 Miners &#40;n&#8239;&#61;&#8239;40&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sex</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">86&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">100&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8211;</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Female&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#60;65 years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">46&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">46&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;751</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#62;65 years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">53&#46;6&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">56&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean &#40;&#177;SD&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">66&#46;2 &#40;&#177;9&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">65&#46;9 &#40;&#177;9&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;4 &#40;&#177;7&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;651&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Smoking</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">No&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8211;</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Active smokers&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">35&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">32&#46;6&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">53&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Ex-smokers&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">55&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">46&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="4" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Adenopathy punctured under EBUS guidance</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4R&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">31&#46;5&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">34&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="4" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;026</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4L&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#46;9&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Other&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">28&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Adenopathy size</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#60;1&#8239;cm&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">55&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;9&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">56&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;861</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#62;1&#8239;cm&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">44&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean &#40;&#177;SD&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;7 &#40;&#177;4&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;8 &#40;&#177;4&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;3 &#40;&#177;3&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;723&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">PET&#47;CT SUV<span class="elsevierStyleInf">max</span></td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#60;2&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">37&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">30&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;449</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>&#62;2&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">70&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean &#40;&#177;SD&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;4 &#40;&#177;3&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;4 &#40;&#177;3&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;3 &#40;&#177;2&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;991&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Neoplasm</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">No&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">73&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">70&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">85&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;000</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Yes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">26&#46;9&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">29&#46;6&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#46;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2809555.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Clinical characteristics of the patients according to their mining exposure&#46;</p>"
        ]
      ]
      2 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0015"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">CI&#58; confidence interval&#46; EBUS&#58; endobronchial ultrasound&#46; NA&#58; not applicable&#46; OR&#58; odds ratio&#46; PET&#47;CT&#58; positron emission tomography with fused computed tomography images&#46; SD&#58; standard deviation&#46; SUV<span class="elsevierStyleInf">max</span>&#58; maximum standardized uptake value&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Risk factor&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mining exposure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR &#40;95&#37; IC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Over 65 years old</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Yes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;577 &#40;1&#46;178&#8211;5&#46;641&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">No&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;320 &#40;0&#46;65&#8211;9&#46;12&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Adenopathy size measured by EBUS</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Greater than 1&#8239;cm</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Yes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;224 &#40;3&#46;134&#8211;16&#46;651&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">No&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;661 &#40;0&#46;245&#8211;11&#46;244&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Adenopathy SUV<span class="elsevierStyleInf">max</span></td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Greater than 2&#46;5</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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          "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Malignancy predictors in mediastinal adenopathies according go the subjects&#8217; history of exposure to mining &#40;results of the regression models&#41;&#46;</p>"
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es en pt

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Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos