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This chatbot has been able to pass the Spanish Resident Medical Intern exam<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> and there is already speculation about its ability to make decisions about diagnoses or preliminary treatment plans.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> In addition, it has been successfully used for synthetic data generation.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The training and testing of natural language processing (NLP) models for the extraction of key information from medical records could be performed using large databases containing synthetic medical records created by chatbots, thus limiting the problems of accessing personal clinical data and avoiding ethical issues. In this paper, we present an analysis of ChatGPT's ability to create clinical histories of fictitious patients who have suffered a hip fracture, in order to use them as a testbed for training NLP algorithms without the ethical and privacy issues that would be involved in using real medical records.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Database and bot</span><p id="par0015" class="elsevierStylePara elsevierViewall">In order to create a fictitious database, 8 variables present in real patients with hip fractures were chosen (sex, age, type of fracture, laterality, anaesthetic risk classification according to the <span class="elsevierStyleItalic">American Society of Anesthesiologists</span> [ASA], whether or not acenocoumarol [Sintrom®] was taken, vital status at hospital discharge - alive or dead - and total number of days in hospital). Based on these 8 input variables, and by randomisation, 1,000 records of hip fracture cases are created. Working with the Python 3 programming language and the <span class="elsevierStyleItalic">OpenAI</span> toolkit, a bot or programme is designed that communicates with ChatGPT through the <span class="elsevierStyleItalic">application programming interface</span> (API) of the OpenAI platform. The GPT-3.5 Turbo chatbot model is chosen in the free version of 9 April 2023. The different writing styles of the clinical progress are controlled with a style variable of 6 options (long-short, technical-simple, optimistic-pessimistic) that are randomised. The “temperature”, which is the parameter of the algorithm that controls the creativity of the chatbot, is also randomised, with values ranging from 0 to 1: when the value of “temperature” is closer to 1, it tends to generate longer and more creative texts, while when it tends to 0, the texts are more deterministic and predictable.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The bot will iterate over ChatGPT the following question 1,000 times, changing the values in the square brackets according to the variables collected in the database: “Write the progress of a traumatology department medical record with a [“STYLE”] of a [“SEX”] patient, [“AGE”] years of age with [“SIDE”] [“TYPE”] hip fracture in the [“NAME OF HOSPITAL”] University Hospital, operated with anaesthetic ASA risk [“ASA”], [“SINTROM”] and who is discharged [“ALIVE/DEAD”] in [“DAYS”] days. Write [“DAYS”] days of progress. For example: first day, anaesthesia visit; second day, seen by internal medicine; third day the wound is checked; fourth day a blood test is requested… and on [“DAYS”] day is discharged [“ALIVE/DEAD”]”. The bot collects and saves the answer given by the chatbot.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Initial analysis of time and text length</span><p id="par0025" class="elsevierStylePara elsevierViewall">An initial analysis is made of the word count in each clinical progress report, as well as the time taken to complete it. As the speed of response of the API varies according to the traffic to which it is subjected, the homogeneity of the input variables over the 1,000 iterations is confirmed, as the heterogeneity of these could lead to doubts about the consistency of the results.</p><p id="par0030" class="elsevierStylePara elsevierViewall">Finally, the interaction between these 2 values with the different variables included in the query and with the “temperature” parameter is analysed.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Style analysis</span><p id="par0035" class="elsevierStylePara elsevierViewall">The different writing styles are analysed through the extraction of different textual features using the NLP toolkit spaCy and the Spanish-optimised <span class="elsevierStyleItalic">pipeline</span> (data transformation code through a sequence of operations) es_core_news_sm. The short-long writing styles are compared according to the word count contained in each clinical progress report. To objectively study the technicality or simplicity of the texts, the average sentence length of the texts is analysed and this average sentence length is compared according to the style that was requested.</p><p id="par0040" class="elsevierStylePara elsevierViewall">In order to assess ChatGPT’s ability to develop different optimistic or pessimistic writing styles, words are extracted from the clinical progress reports that can express positivity or negativity in the progress of the fictitious patient. The number of these words contained in each text is calculated and the value is obtained by subtracting the number of positive words from the number of negative words. If it is greater than or equal to 2, the progress report is considered to be “optimistic”; on the other hand, if this operation is less than −2, the progress report is considered to be “pessimistic”. Between these two values, the text will be “neutral”. To validate this mood score, the result of this will be evaluated with the life variable of the fictitious patients, assuming that patients who die should present more pessimistic clinical progress reports than those who are discharged alive.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Extraction of key data</span><p id="par0045" class="elsevierStylePara elsevierViewall">In order to study the ability of ChatGPT to produce clinical progress reports containing information not previously offered to the model, we analysed whether these texts contained any mention of the 4 aspects most commonly present during a hip fracture hospitalisation: rehabilitation, infection, thromboprophylaxis and osteoporosis. The relationship of these 4 aspects is compared with the different characteristics of the fictitious patients. The chatbot’s ability to indicate internal fixation or hip arthroplasty to the fictitious patients according to the type of fracture (intracapsular-arthroplasty or extracapsular-internal fixation) will also be analysed.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Statistical analysis</span><p id="par0050" class="elsevierStylePara elsevierViewall">Frequencies and percentages for categorical variables and means for continuous variables are used for a descriptive analysis of the sample. The analysis of the homogeneous distribution of the input variables is carried out with the Kolmogórov-Smirnov test for dichotomous variables, Kruskal-Wallis for polynomial variables and Pearson's coefficient for continuous numerical variables. A p value<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 would indicate a statistical relationship between the variables and the iterations and, therefore, it could be considered that there is heterogeneity in the distribution of the former across the latter. In case of heterogeneity, an analysis of covariance (ANCOVA) will be performed to rule out that this could be a confounding factor in the data.</p><p id="par0055" class="elsevierStylePara elsevierViewall">Fisher’s exact test is used to assess the association of dichotomous variables. To assess the association of polynomial variables, the Mann-Whitney U test is used. In the case of numerical variables, Student’s t-tests, analysis of variance or Pearson’s coefficient are used depending on whether the comparison variable is dichotomous, polynomial or numerical, respectively.</p><p id="par0060" class="elsevierStylePara elsevierViewall">The Python 3 code required to carry out this study will be developed and executed on the Jupyter programming platform. The SPSS® 29.0 statistical package will be used for the statistical work. A relationship will be considered statistically significant if p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Results</span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Initial analysis of time and text length</span><p id="par0065" class="elsevierStylePara elsevierViewall">ChatGPT took 12<span class="elsevierStyleHsp" style=""></span>h and 40<span class="elsevierStyleHsp" style=""></span>min to iterate and create the clinical progress reports of the 1,000 fictitious patients. <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows an example of this iteration on the Jupyter <span class="elsevierStyleItalic">notebook</span>. The speed of text creation was not homogeneous, but a trough can be observed between iterations 165 and 570, during which the generation time was shorter. The file generated in JSON format was 1.71<span class="elsevierStyleHsp" style=""></span>MB, with 195,074 words. The shortest clinical progress report had 45 words and the longest had 1,090 words. The length was homogeneous throughout the iterations, although from iteration 675 onwards there is a tendency to generate longer texts (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). When inferring the results of time and word count with the variables introduced, it was observed that both the female sex and discharge with a death outcome had shorter clinical progress reports (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01) and shorter generation time (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01). The progress reports of admissions longer than 5 days also had higher word count (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01) and generation time (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01). No significant differences were found with other variables (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">The distribution of the input variables was homogeneous across the 1,000 iterations, except in the case of the vital status variable at discharge (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.04). In the case of the sex variable, it also appears to have a tendency towards heterogeneity (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.08). Discharge with death and female sex tended to appear more often at the beginning of the iterations, while male sex and discharge alive appeared more frequently in the final iterations. ANCOVA analysis showed that this heterogeneity affected the life status variable (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.18 and 0.24 for generation time and total word count, respectively), but not the sex variable (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01 for generation time and total word count).</p><p id="par0075" class="elsevierStylePara elsevierViewall">No statistical relationship is observed between the “temperature” parameter and the generation time of the clinical progress report (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.68) or the word count (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.34).</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Style analysis</span><p id="par0080" class="elsevierStylePara elsevierViewall">The texts where ChatGPT was suggested a short style had a mean length of 182.2 words, while those with a long style had a mean length of 194.54 words (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.19). On the other hand, the average sentence length of the technical progress reports was 15.97 words compared to 15.48 words for the simple progress reports (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.09).</p><p id="par0085" class="elsevierStylePara elsevierViewall">20.6% of patients who died during admission had a “pessimistic” mood <span class="elsevierStyleItalic">score</span> compared to 3.5% of patients who were discharged alive (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.048). This indicates that there may be some concordance between <span class="elsevierStyleItalic">score</span> and mood. However, no significant differences were found between this <span class="elsevierStyleItalic">score</span> and the optimistic and pessimistic styles suggested to ChatGPT.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Extraction of key data</span><p id="par0090" class="elsevierStylePara elsevierViewall">ChatGPT produced information relating to the admission of a patient with a hip fracture, which was not suggested a priori by the input variables. Rehabilitation/physiotherapy was mentioned in 73.6% of cases, and infection was mentioned in 26.5% of cases. However, the chatbot was unable to relate fracture-episodes to thrombosis-episodes or the need for thromboprophylaxis (3.9%) and did not consider osteoporosis as a condition to be taken into account in the clinical progress report (one case out of 1,000) (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>). In terms of surgical options, the chatbot opted for hip arthroplasty in 15.6% of cases, and for internal fixation in 16.5%. There were 13 cases (1.3%) where both prosthesis and internal fixation were indicated. However, ChatGPT was not able to correctly indicate these types of surgery according to fracture type (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). The mean number of references to type of surgery, rehabilitation, infection, thromboprophylaxis or osteoporosis was 1.38 per patient, and no significant differences were found in this value with respect to the variables entered, not even in those that did show such a significant difference in relation to time or word count (sex, days of admission).</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Discussion</span><p id="par0095" class="elsevierStylePara elsevierViewall">Since ChatGPT was introduced to the general public in 2022, a large body of medical literature has emerged that discusses different uses of this technology in medical practice and research.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> There has also been considerable literature on the implications of this chatbot for the generation of synthetic data.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> ChatGPT is based on a NLP-specific algorithm called <span class="elsevierStyleItalic">Transformer</span>, first referenced in 2017.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a><span class="elsevierStyleItalic">Transformer</span>, unlike some NLP libraries (spaCy, NLTK, etc.), is not only able to understand the general meaning of a word, but also to conceptualise it within the sentence in which it is embedded.<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> In this way, <span class="elsevierStyleItalic">Transformers</span> can create a perfectly understandable machine-human conversation. However, all the information used by the <span class="elsevierStyleItalic">Transformers</span> both for the understanding of human <span class="elsevierStyleItalic">input</span> and for the generation of <span class="elsevierStyleItalic">output</span> must have been included in the training of this algorithm. This means that chatbots, at present, do not actually generate new information, but reuse data with which they have been previously trained.</p><p id="par0100" class="elsevierStylePara elsevierViewall">The results of this work are, to say the least, disconcerting. On the one hand, without being a chatbot specifically for medical language, it has been able to generate 1,000 coherent and comprehensible clinical progress reports that perfectly meet the requirements that were requested with the input variables. The combination of coherence and comprehensibility of the information provided by the chatbot may explain the results of a study on health chats, which concluded that the answers given by ChatGPT were more empathetic and of higher quality than those given by health professionals.<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> No objective stylistic differences in text generation have been found according to the parameter “temperature” or the style variable. Although there are articles that have analysed the style of English text generated by ChatGPT according to sentence length or vocabulary complexity, with significant results,<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> we have not found any article applied to Spanish. On the other hand, a poor ability to relate fracture-episodes to different pathophysiological and perioperative treatment aspects of hip fracture patients is observed. At this stage of its development, the chatbot is not able to relate an extracapsular or intracapsular hip fracture to the treatment of the fracture (internal fixation or arthroplasty). This inability of ChatGPT to make a general interpretation of the data provided has already been observed in other fields of clinical research.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a></p><p id="par0105" class="elsevierStylePara elsevierViewall">Another striking aspect of the results is the gender bias in terms of generation time and word count generated. Women’s progress reports were much shorter and took less time to generate. The explanation must lie in the chatbot's internal algorithm, <span class="elsevierStyleItalic">Transformer</span>, which retrieves from its data repositories all available information about hip fractures, which occur predominantly in females. However, we do not know what the reason for this imbalance might be. Therefore, in the current state of technology, these biases must be taken into account when drawing conclusions about the results obtained with ChatGPT.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">One of the strengths of this study is the large number of progress reports with which it has been possible to work. Among the weaknesses, we must take into account the possible bias that may have occurred in the speed of generation of clinical progress reports. This speed was not homogeneous and was influenced by the traffic that the ChatGPT API interface was receiving at any given time. However, it should be noted that the randomisation of the input variables has ensured that all these variables are homogeneously distributed throughout the iteration process, except in two cases (sex and vital status at discharge), of which only in one case was there an influence of heterogeneity in the results (vital status at discharge). On the other hand, the objective analysis of writing styles has been based solely and exclusively on structural features of the text and keyword extraction, but more complex models of natural language analysis have not been applied, such as the bidirectional encoder representations from transformers (BERT), whose use is not yet widespread in the natural language processing of texts related to orthopaedic surgery and traumatology.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Conclusions</span><p id="par0115" class="elsevierStylePara elsevierViewall">ChatGPT has proven to be able to massively generate coherent and comprehensible clinical progress reports that follow a clear logic both in terms of the structure of a hospital admission progress report and in terms of the pathophysiological characteristics of hip fracture. Neither the parameter “temperature” nor the style variable was observed to have contributed to altering the writing style of these texts. However, beyond coherence, comprehensibility and logic, ChatGPT did not demonstrate a deep understanding of the reality of hip fracture and failed to create new content not specified in the input variables. It is important to remember that this is a non-specific tool for medical science related topics, and that it is evolving rapidly, so it may be that in the future ChatGPT or other chatbots will be able to create synthetic medical records that mimic almost exactly any real process in the hospital clinical environment. At present, we do not recommend using this technology and procedure as a testbed for training <span class="elsevierStyleItalic">machine learning</span> algorithms for clinical research purposes in orthopaedic surgery and traumatology.</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Funding</span><p id="par0120" class="elsevierStylePara elsevierViewall">None.</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Conflict of interest</span><p id="par0125" class="elsevierStylePara elsevierViewall">None.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:8 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 1 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Database and bot" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Initial analysis of time and text length" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Style analysis" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Extraction of key data" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 2 => array:3 [ "identificador" => "sec0040" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Initial analysis of time and text length" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Style analysis" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Extraction of key data" ] ] ] 3 => array:2 [ "identificador" => "sec0060" "titulo" => "Discussion" ] 4 => array:2 [ "identificador" => "sec0065" "titulo" => "Conclusions" ] 5 => array:2 [ "identificador" => "sec0070" "titulo" => "Funding" ] 6 => array:2 [ "identificador" => "sec0075" "titulo" => "Conflict of interest" ] 7 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-09-21" "fechaAceptado" => "2023-11-22" "multimedia" => array:5 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1459 "Ancho" => 2175 "Tamanyo" => 499507 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Example of a clinical progress report generated by ChatGPT (iteration no. 14: 75-year-old female patient, ASA III, not taking Sintrom®, who presents with extracapsular fracture of the left hip and who dies on the eighth day. The clinical progress report was requested to be written in an optimistic style and the temperature parameter was set to 0.77). It took 76.03<span class="elsevierStyleHsp" style=""></span>s to generate and has 251 words.</p>" ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 3061 "Ancho" => 2175 "Tamanyo" => 387948 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0010" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Relationship between iterations and the generation time of clinical progress reports (top) and their total word count (bottom).</p>" ] ] 2 => array:8 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1418 "Ancho" => 2175 "Tamanyo" => 110278 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0015" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Frequency of occurrence of key data in clinical progress reports.</p>" ] ] 3 => array:8 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1400 "Ancho" => 2175 "Tamanyo" => 144106 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Surgical indications suggested by ChatGPT according to fracture type.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "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">Input variables \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">Average generation time \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">Average word count \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Sex</span><a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">a,b</span></a> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Male \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">50.8 \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">212.24 \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"><span class="elsevierStyleHsp" style=""></span>Female \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">40.59 \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">178.57 \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"><span class="elsevierStyleItalic">Age</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">c</span></a> \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">−0.005 \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">0.001 \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"><span class="elsevierStyleItalic">Type of fracture</span> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Extracapsular \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">47.33 \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">197.76 \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"><span class="elsevierStyleHsp" style=""></span>Intracapsular \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">43.87 \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">192.38 \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"><span class="elsevierStyleItalic">Laterality</span> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Right \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">44.53 \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">190.68 \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"><span class="elsevierStyleHsp" style=""></span>Left \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">46.64 \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">199.34 \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"><span class="elsevierStyleItalic">ASA Risk</span> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>II \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">46.48 \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">195.6 \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"><span class="elsevierStyleHsp" style=""></span>III \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">44.73 \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">195.55 \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"><span class="elsevierStyleItalic">Sintrom®</span> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Yes \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">45.57 \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">196.54 \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"><span class="elsevierStyleHsp" style=""></span>No \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">45.61 \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">194.7 \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"><span class="elsevierStyleItalic">Vital status at discharge</span><a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">a,d</span></a> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Alive \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">46.02 \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">196.16 \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"><span class="elsevierStyleHsp" style=""></span>Dead \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">32.63 \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">158.58 \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"><span class="elsevierStyleItalic">Days of admission</span><a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>5 \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">39.86 \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">172.88 \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"><span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>5 \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">50.18 \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">212.8 \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"><span class="elsevierStyleItalic">“Temperature”</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">c</span></a> \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">0.01 \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">−0.03 \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"><span class="elsevierStyleItalic">Style</span> \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"> \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"> \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"><span class="elsevierStyleHsp" style=""></span>Short \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">40.72 \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">182.2 \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"><span class="elsevierStyleHsp" style=""></span>Long \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">45.4 \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">194.54 \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"><span class="elsevierStyleHsp" style=""></span>Technical \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">45.63 \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">193.72 \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"><span class="elsevierStyleHsp" style=""></span>Simple \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">46.87 \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">200.6 \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"><span class="elsevierStyleHsp" style=""></span>Optimistic \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">47.96 \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">202.99 \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"><span class="elsevierStyleHsp" style=""></span>Pessimistic \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">46.69 \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">195.27 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3563292.png" ] ] ] "notaPie" => array:4 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Statistically significant.</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Statistically significant after ANCOVA.</p>" ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "c" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Pearson correlation.</p>" ] 3 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "d" "nota" => "<p class="elsevierStyleNotepara" id="npar0020">Statistically not significant after ANCOVA.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Relationship between the input and style variables and the generation time and word count.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:16 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "¿Qué son los datos sintéticos? Datos generados para ayudar a tu estrategia de IA" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "M. Korolov" ] ] ] ] ] "host" => array:2 [ 0 => array:1 [ "Revista" => array:2 [ "tituloSerie" => "CIO Spain" "fecha" => "2022" ] ] 1 => array:1 [ "WWW" => array:1 [ "link" => "https://www.ciospain.es/big-data/que-son-los-datos-sinteticos-datos-generados-para-ayudar-a-tu-estrategia-de-ia" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Synthetic data could be better than real data" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "N. 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Journal Information
Vol. 162. Issue 11.
Pages 549-554 (June 2024)
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Vol. 162. Issue 11.
Pages 549-554 (June 2024)
Special Article
Massive generation of synthetic medical records with ChatGPT: An example in hip fractures
Generación masiva de historias clínicas sintéticas con ChatGPT: un ejemplo en fractura de cadera
Isidoro Calvo-Lorenzo
, Iker Uriarte-Llano
Corresponding author
Servicio de Cirugía Ortopédica y Traumatología, Hospital Universitario Galdakao Usansolo, Galdakao, Vizcaya, Spain
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