{"id":"W4386335440","doi":"10.2196/preprints.48763","title":"Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agricultural Research Institute of Ontario; University of Ottawa","funders":"","keywords":"Checklist; Guideline; Inclusion (mineral); Systematic review; Inclusion and exclusion criteria; Quality (philosophy); Quality Score; Set (abstract data type); Computer science; Medicine; Medical physics; MEDLINE; Data science; Psychology; Pathology; Alternative medicine; Engineering; Political science; Operations management","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":["metaresearch"],"category_scores_codex":[0.4215477,0.003765891,0.009624681,0.03900111,0.004774606,0.01918919,0.01356615,0.01321324,0.04420921],"category_scores_gemma":[0.7731998,0.006202062,0.02083453,0.02747659,0.006407773,0.01141479,0.01486141,0.01298208,0.03722674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01213757,"about_ca_system_score_gemma":0.06128059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006313102,"about_ca_topic_score_gemma":0.004981577,"domain_scores_codex":[0.3717777,0.2140217,0.3481155,0.00876179,0.05268936,0.004633871],"domain_scores_gemma":[0.09601764,0.3899834,0.1306779,0.05817492,0.3211748,0.003971235],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008227573,0.0001436291,0.002089365,0.160026,0.00123009,0.0003081197,0.002179998,0.0008281746,0.001321197,0.009532804,0.7115047,0.1100131],"study_design_scores_gemma":[0.0008233705,0.0002445212,0.004433226,0.2468605,0.001387583,0.0004069892,0.001558902,0.0009454144,0.00254533,0.008962591,0.7315016,0.0003299081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"methods","genre_scores_codex":[0.004028196,0.07128961,0.2281102,0.1168938,0.06972653,0.2359409,0.2220652,0.01398621,0.03795941],"genre_scores_gemma":[0.0112969,0.04691963,0.3828216,0.02796277,0.007201091,0.4281097,0.08144902,0.003477325,0.01076187],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5784522,"threshold_uncertainty_score":0.7133346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6573782801423922,"score_gpt":0.5367903288680909,"score_spread":0.1205879512743013,"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."}}