The program is effective and feasible to be applied as a regular formative activity. Further research is needed to assess whether this training program is applicable to students in more advanced educational levels and if it has any additional outcomes.

BackgroundThe Jefferson Scale of Physician Empathy is the most widely used instrument to measure empathy in the doctor-patient relationship. This work pursued cultural adaptation and validation of the original scale, in its health professions version (JSE-HP), for medical students who participate in an Early Clerkship Immersion Programme of a Spanish university.MethodsThe questionnaire was replied by 506 1st, 2nd, 3rd and 5th year medical students from Universidad Francisco de Vitoria, Madrid, in 2014 and 2016. Internal consistency was analysed by means of Cronbach’s alpha, and reliability by means of test-retest using the intraclass correlation coefficient and the Bland-Altman method. The construct validity was checked by means of confirmatory factor analysis and association with other empathy-related variables. Criterion validity was compared using Davis’ Interpersonal Reactivity Index.ResultsCronbach’s alpha was 0.82 (range 0.80–0.85). Item-total score correlations were positive and significant (median 0.45, p <  0.01). The test-retest intraclass correlation coefficient was 0.68 (0.42–0.82). The factor analysis confirmed the three original factors: “perspective taking”, “compassionate care” and “standing in the patient’s shoes”. Women and students who preferred specialities focused on persons obtained the best scores. The JSE-HP scores were positively correlated with Interpersonal Reactivity Index, personality traits were associated with empathy, clinical interview skills and Objective Structured Clinical Examinations.ConclusionThe results support the validity and reliability of JSE-HP applied to Spanish medical students.Electronic supplementary materialThe online version of this article (10.1186/s12909-018-1309-9) contains supplementary material, which is available to authorized users.

Information about the association of energy and iron-metabolising genes with endurance performance is scarce. The objective of this investigation was to compare the frequencies of polymorphic variations of genes involved in energy generation and iron metabolism in elite endurance athletes vs. non-athlete controls. Genotype frequencies in 123 male elite endurance athletes (75 professional road cyclists and 48 elite endurance runners) and 122 male non-athlete participants were compared by assessing four genetic polymorphisms: AMPD1 c.34C/T (rs17602729), PPARGC1A c.1444G/A (rs8192678) HFEH63D c.187C/G (rs1799945) and HFEC282Y c.845G/A (rs1800562). A weighted genotype score (w-TGS: from 0 to 100 arbitrary units; a.u.) was calculated by assigning a corresponding weight to each polymorphism. In the non-athlete population, the mean w-TGS value was lower (39.962±14.654 a.u.) than in the group of elite endurance athletes (53.344±17.053 a.u). The binary logistic regression analysis showed that participants with a w-TGS>38.975 a.u had an odds ratio of 1.481 (95%CI: 1.244-1.762; p<0.001) for achieving elite athlete status. The genotypic distribution of polymorphic variations involved in energy generation and iron metabolism was different in elite endurance athletes vs. controls. Thus, an optimal genetic profile in these genes might contribute to physical endurance in athlete status. Novelty 1. Genetic profile in energy generation and iron-metabolising genes in elite endurance athletes is different than non-athlete´s. 2. There is an implication of an "optimal" genetic profile in the selected genes favouring endurance sporting performance.

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Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.