{"id":"W4399772087","doi":"10.2196/60384","title":"Authors’ Response to Peer Reviews of “Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis”","year":2024,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Medicine; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03249082,0.001209573,0.002653948,0.002895056,0.003436897,0.006762702,0.002845283,0.0134257,0.0503113],"category_scores_gemma":[0.3473339,0.001084858,0.002852822,0.00285986,0.001994313,0.003313405,0.003528887,0.01508331,0.04574194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003405806,"about_ca_system_score_gemma":0.0092425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003996712,"about_ca_topic_score_gemma":0.006024455,"domain_scores_codex":[0.9563687,0.01086432,0.008115038,0.002981533,0.02002677,0.001643631],"domain_scores_gemma":[0.5817608,0.07723822,0.02097121,0.01200305,0.3009287,0.007097953],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003718641,0.000006595448,0.0001587258,0.0001620705,0.00001766706,0.00005371584,0.00004114442,0.00002380457,0.00004114025,0.0001430129,0.9954734,0.003841414],"study_design_scores_gemma":[0.00006313062,0.00003118368,0.001063605,0.0007741434,0.00004243667,0.0002299009,0.0002412601,0.0003875279,0.0003217091,0.0007418679,0.9960336,0.00006951524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.0005001814,0.002689184,0.001504326,0.5481239,0.4404169,0.0002145005,0.002193291,0.0006594526,0.00369822],"genre_scores_gemma":[0.01146622,0.005579334,0.003765133,0.6173196,0.3091569,0.001013061,0.002287205,0.001221356,0.04819116],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.9675092,"threshold_uncertainty_score":0.1718299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03389387698986936,"score_gpt":0.3062406549357081,"score_spread":0.2723467779458387,"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."}}