{"id":"W4401596293","doi":"10.51731/cjht.2024.951","title":"Canadian Medical Imaging Inventory 2022–2023: MRI","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Medicine; Population; Magnetic resonance imaging; Nuclear medicine; Medical imaging; Demography; Radiology; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002466288,0.001258709,0.001055123,0.009367158,0.004007504,0.004813721,0.003676653,0.001442914,0.0690169],"category_scores_gemma":[0.01796135,0.0008905776,0.001205992,0.01685837,0.0007464531,0.001586956,0.001924496,0.002075527,0.02251527],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04498006,"about_ca_system_score_gemma":0.1840784,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9693342,"about_ca_topic_score_gemma":0.9786837,"domain_scores_codex":[0.9917469,0.0003004098,0.0003534741,0.0003626771,0.006268125,0.0009684297],"domain_scores_gemma":[0.9478583,0.001081709,0.00159295,0.0006547801,0.04319179,0.005620417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003834086,0.00002012005,0.004192529,0.0001733616,0.00002150151,0.00003495989,0.00002620113,0.0001120795,0.00006016255,0.001552385,0.9683219,0.02544647],"study_design_scores_gemma":[0.0000241978,0.00001346038,0.02507952,0.0002851833,0.00002170125,0.0001020712,0.00009833737,0.0003583356,0.0001133968,0.0003145542,0.9735481,0.00004126088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005095835,0.008476613,0.002506448,0.0218886,0.002929586,0.001071355,0.5875486,0.002764573,0.3677184],"genre_scores_gemma":[0.05821392,0.02070569,0.01734071,0.01838804,0.001495724,0.001295819,0.686062,0.001356301,0.1951418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.95502,"threshold_uncertainty_score":0.3263547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416932765813073,"score_gpt":0.2982021297440637,"score_spread":0.284032802085933,"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."}}