{"id":"W4410523690","doi":"10.1136/bmjoq-2025-qshu.185","title":"185 Making the mandatory meaningful – leveraging regulatory requirements to achieve system improvement","year":2025,"lang":"en","type":"article","venue":"","topic":"Biomedical Ethics and Regulation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Computer science; Risk analysis (engineering); Business","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":[],"consensus_categories":[],"category_scores_codex":[0.02421924,0.0003630503,0.000617348,0.001306308,0.002563308,0.003644643,0.001396834,0.001777932,0.01406653],"category_scores_gemma":[0.09028072,0.0003647748,0.0008925071,0.0007644129,0.002104274,0.001884237,0.003240159,0.002973903,0.004258682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004041824,"about_ca_system_score_gemma":0.01080672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007326637,"about_ca_topic_score_gemma":0.01350486,"domain_scores_codex":[0.9742162,0.0089289,0.00139297,0.001362969,0.01087496,0.003224159],"domain_scores_gemma":[0.9206713,0.03249218,0.01069257,0.007641568,0.02320817,0.005294301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002820656,0.009007091,0.3981782,0.000495613,0.0003297401,0.0003631488,0.01329012,0.003994068,0.01290443,0.03177598,0.04938537,0.4774556],"study_design_scores_gemma":[0.0001298407,0.003832131,0.9350176,0.0001461448,0.0001012359,0.0001101554,0.005168848,0.00592463,0.01540394,0.01248246,0.02154356,0.0001394443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.8985253,0.0001311989,0.01774489,0.008358208,0.0003673925,0.0009458832,0.0007265519,0.0007169336,0.07248359],"genre_scores_gemma":[0.9836749,0.00002936711,0.007445994,0.0006669331,0.00004281766,0.0004705481,0.0002449169,0.00004842479,0.007375957],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.02421924,"threshold_uncertainty_score":0.1280851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02615029543222294,"score_gpt":0.3207264988024673,"score_spread":0.2945762033702444,"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."}}