{"id":"W3092538788","doi":"10.1016/j.patter.2020.100119","title":"Inference and Prediction Diverge in Biomedicine","year":2020,"lang":"en","type":"article","venue":"Patterns","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Seventh Framework Programme; RWTH Aachen University; National Institute on Aging; Deutsche Forschungsgemeinschaft; National Institutes of Health; National University of Singapore; Amazon Web Services; Institut national de recherche en informatique et en automatique (INRIA); Canadian Institute for Advanced Research; Google","keywords":"Biomedicine; Inference; Artificial intelligence; Computer science; Biology; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.04423448,0.00121842,0.002020909,0.003723679,0.001241463,0.006131007,0.002217547,0.003513979,0.003336111],"category_scores_gemma":[0.1463125,0.00097693,0.00126083,0.002608072,0.01132001,0.01016169,0.006428928,0.006084958,0.0008637251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002801111,"about_ca_system_score_gemma":0.003601074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00404565,"about_ca_topic_score_gemma":0.002364884,"domain_scores_codex":[0.9728366,0.01940705,0.001045542,0.003329078,0.002955412,0.0004263727],"domain_scores_gemma":[0.8743742,0.1124416,0.003213967,0.006614186,0.002357331,0.0009987036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004756456,0.000134314,0.02877731,0.001076189,0.0008227809,0.0003109806,0.002150143,0.06101793,0.0006018654,0.615671,0.008790474,0.2801715],"study_design_scores_gemma":[0.00003657527,0.0000568834,0.002295132,0.0003972249,0.00005720725,0.00008386721,0.000233373,0.05664163,0.0003050508,0.9321537,0.007699745,0.00003947972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04525327,0.04432034,0.8040568,0.08717999,0.001338016,0.0002521003,0.0008102683,0.000642543,0.01614671],"genre_scores_gemma":[0.6713908,0.01806339,0.2907671,0.01210895,0.002905075,0.0006584642,0.0008466498,0.0002694661,0.002990155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04423448,"threshold_uncertainty_score":0.233937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03130443943549634,"score_gpt":0.2871782830493894,"score_spread":0.255873843613893,"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."}}