{"id":"W3097079650","doi":"10.36834/cmej.71154","title":"Re: CaRMS at 50","year":2020,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Cardiovascular Society","funders":"","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00327132,0.001684781,0.002553851,0.00621481,0.002104633,0.005774344,0.00243867,0.01009516,0.8327888],"category_scores_gemma":[0.02433716,0.001059866,0.001997643,0.002393722,0.002015185,0.002055151,0.003795049,0.005081425,0.7554541],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003136186,"about_ca_system_score_gemma":0.006181145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006334055,"about_ca_topic_score_gemma":0.01874399,"domain_scores_codex":[0.9967383,0.0005278394,0.0002577537,0.0005070569,0.001539383,0.0004296984],"domain_scores_gemma":[0.9893902,0.002531535,0.0007014023,0.001425946,0.003608759,0.002342167],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000656814,0.00001841438,0.0001040716,0.0002020661,0.00001211896,0.00009996,0.000009591206,0.00002814977,0.0001148827,0.0007195292,0.9652821,0.03334347],"study_design_scores_gemma":[0.00007956526,0.0000168422,0.0006567944,0.0006152731,0.0000135618,0.0001284344,0.00003484056,0.0001072318,0.00007912403,0.001478363,0.9967727,0.00001729284],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.0004660716,0.006960671,0.0009863978,0.06301656,0.07390349,0.0009027455,0.005890514,0.004253206,0.8436204],"genre_scores_gemma":[0.002614131,0.001475485,0.0004987132,0.024476,0.011,0.0006114846,0.001020166,0.0006438362,0.9576602],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9968638,"threshold_uncertainty_score":0.2385064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5845296122357233,"score_gpt":0.5142751438357669,"score_spread":0.07025446839995642,"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."}}