Guillén Grima, Francisco
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Guillén Grima
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Francisco
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Ciencias de la Salud
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Publication Open Access Relationship between perceived body weight and body mass index based on self- reported height and weight among university students: a cross-sectional study in seven European countries(BioMed Central, 2010) Mikolajczyk, Rafael T.; Maxwell, Annette E.; El Ansari, Walid; Stock, Christiane; Petkeviciene, Janina; Guillén Grima, Francisco; Ciencias de la Salud; Osasun ZientziakBackground: Despite low rates of obesity, many university students perceive themselves as overweight, especially women. This is of concern, because inappropriate weight perceptions can lead to unhealthy behaviours including eating disorders. Methods: We used the database from the Cross National Student Health Survey (CNSHS), consisting of 5, 900 records of university students from Bulgaria, Denmark, Germany, Lithuania, Poland, Spain and Turkey to analyse differences in perceived weight status based on the question: "Do you consider yourself much too thin, a little too thin, just right, a little too fat or much too fat?". The association between perceived weight and body mass index (BMI) calculated from self-reported weight and height was assessed with generalized non-parametric regression in R library gam. Results: Although the majority of students reported a normal BMI (72-84% of males, 65-83% of females), only 32% to 68% of students considered their weight "just right". Around 20% of females with BMI of 20 kg/m(2) considered themselves "a little too fat" or "too fat", and the percentages increased to 60% for a BMI of 22.5 kg/m(2). Male students rarely felt "a little too fat" or "too fat" below BMI of 22.5 kg/m(2), but most felt too thin with a BMI of 20 kg/m(2). Conclusions: Weight ideals are rather uniform across the European countries, with female students being more likely to perceive themselves as "too fat" at a normal BMI, while male students being more likely to perceive themselves as "too thin". Programs to prevent unhealthy behaviours to achieve ill-advised weight ideals may benefit students.Publication Open Access Evaluating the efficacy of ChatGPT in navigating the spanish medical residency entrance examination (MIR): promising horizons for AI in clinical medicine(MDPI, 2023) Guillén Grima, Francisco; Guillén Aguinaga, Sara; Guillén Aguinaga, Laura; Alas Brun, Rosa María; Onambele, Luc; Ortega-Leon, Wilfrido; Montejo, Rocío; Aguinaga Ontoso, Enrique; Barach, Paul; Aguinaga Ontoso, Inés; Ciencias de la Salud; Osasun ZientziakThe rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model’s overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. Material and methods: We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4’s new image analysis capability. Results: GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as “error requiring intervention to sustain life” and “error resulting in death”, had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model’s high success rate is commendable, understanding the error severity is critical, especially when considering AI’s potential role in real-world medical practice and its implications for patient safety.