{"id":"W4412200350","doi":"10.51731/cjht.2025.1166","title":"MRI Usage Patterns by Clinical Specialty Across Canada: Geographic and Temporal Trends","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Radiology practices and education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Specialty; Geography; Cartography; Data science; Medicine; Computer science; Family medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002361308,0.000257142,0.0005230253,0.003490413,0.002023311,0.001928756,0.001797338,0.000569292,0.002623463],"category_scores_gemma":[0.009774392,0.0004362593,0.000916957,0.01207535,0.001170018,0.001138751,0.001360343,0.001748287,0.0002885424],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03020295,"about_ca_system_score_gemma":0.04351911,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9902517,"about_ca_topic_score_gemma":0.9940975,"domain_scores_codex":[0.9965488,0.0002591555,0.0003863383,0.0005198348,0.001352309,0.000933608],"domain_scores_gemma":[0.982882,0.001682383,0.003871629,0.0004414389,0.008933236,0.002189315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000804521,0.00002018443,0.9716166,0.0002956489,0.0001264527,0.0001002634,0.002164733,0.0002483095,0.0002112521,0.0004260793,0.007801319,0.01690871],"study_design_scores_gemma":[0.00000350895,0.00001382819,0.9919845,0.000179495,0.00003158856,0.00009941673,0.003141033,0.0003535473,0.00007295018,0.00005477114,0.004043086,0.00002214675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9019485,0.01435549,0.001279925,0.02200522,0.0001386973,0.0001604683,0.04762519,0.0002019992,0.01228451],"genre_scores_gemma":[0.9837261,0.004929242,0.0009481313,0.001690243,0.00004535783,0.00006071841,0.007046586,0.00005178001,0.001501854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9697971,"threshold_uncertainty_score":0.2191387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0336332750854452,"score_gpt":0.3813749311619503,"score_spread":0.347741656076505,"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."}}