{"id":"W4389958532","doi":"10.1097/acm.0000000000005600","title":"Finding Medicine’s Moneyball: How Lessons From Major League Baseball Can Advance Assessment in Precision Education","year":2023,"lang":"en","type":"article","venue":"Academic Medicine","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; The Wilson Centre; Sinai Health System; University of Toronto","funders":"","keywords":"Leverage (statistics); Analytics; League; Computer science; Data science; Precision medicine; Perspective (graphical); Psychological intervention; Medical education; Artificial intelligence; Medicine; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02591988,0.001214397,0.0008276385,0.004560463,0.003763307,0.01254921,0.003168204,0.00492433,0.009340161],"category_scores_gemma":[0.07952665,0.0006873741,0.001396932,0.001976351,0.007618989,0.01412295,0.009223669,0.01035587,0.004079134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005002182,"about_ca_system_score_gemma":0.01225467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144263,"about_ca_topic_score_gemma":0.03705514,"domain_scores_codex":[0.9872965,0.007327073,0.0005941571,0.001152527,0.002797753,0.0008320828],"domain_scores_gemma":[0.9441009,0.03763551,0.001733366,0.003101995,0.00834334,0.005085014],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002061704,0.0005163962,0.02630861,0.001248451,0.0001367894,0.0006840545,0.01359194,0.002061938,0.0006611387,0.0619817,0.1505293,0.7420734],"study_design_scores_gemma":[0.0001297692,0.0006612237,0.0252669,0.008112551,0.0001984044,0.001188926,0.03289532,0.006741935,0.001893725,0.4090767,0.513522,0.0003125202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03743425,0.03008033,0.106888,0.7113373,0.01322117,0.0005434953,0.0006606473,0.00162376,0.09821104],"genre_scores_gemma":[0.5167257,0.04283314,0.3068024,0.08976406,0.007630765,0.0008587808,0.00114557,0.0009339552,0.03330562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9740801,"threshold_uncertainty_score":0.137079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6535621597203349,"score_gpt":0.5859176848697508,"score_spread":0.06764447485058411,"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."}}