{"id":"W4407926290","doi":"10.2196/63602","title":"Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Institute of General Medical Sciences","keywords":"Preprint; Medical education; Computer science; Psychology; Medicine; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006082419,0.0002096512,0.0002266352,0.0005453585,0.00007756642,0.00005352312,0.0003549086,0.0002244878,0.0003340512],"category_scores_gemma":[0.0009564636,0.0002080229,0.0000352539,0.001281759,0.00004587287,0.000152251,0.00007757288,0.0007021843,0.00007449986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003630531,"about_ca_system_score_gemma":0.003577051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005433262,"about_ca_topic_score_gemma":0.000369727,"domain_scores_codex":[0.9980546,0.0000751658,0.0004971822,0.0003709086,0.000659489,0.0003426772],"domain_scores_gemma":[0.9987108,0.00009124439,0.00002021451,0.0004086643,0.00006922036,0.0006999108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007965361,0.00413757,0.006851909,0.0005537669,0.00005185307,0.0001353121,0.006469086,0.0001251615,0.00003537297,0.0012014,0.4374899,0.5429407],"study_design_scores_gemma":[0.003495245,0.000265743,0.1150076,0.004780076,0.0001485007,0.002243332,0.04983425,0.06114097,0.00009343117,0.003979653,0.7569572,0.002053953],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9094535,0.0004687184,0.007769389,0.07046095,0.007994317,0.0009587153,0.000001635334,0.0004285767,0.002464249],"genre_scores_gemma":[0.9915888,0.00003084408,0.0005075527,0.00553969,0.0005357205,0.0008703381,0.00006684547,0.00002773625,0.000832497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5408868,"threshold_uncertainty_score":0.8482929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009154477282975476,"score_gpt":0.3306574814061526,"score_spread":0.3215030041231771,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}