{"id":"W4401596249","doi":"10.51731/cjht.2024.948","title":"Canadian Medical Imaging Inventory 2022–2023: Provincial and Territorial Overview","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Radiology practices and education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Alberta","keywords":"Staffing; Population; Economic shortage; Workforce; Medical imaging; Medicine; Nuclear medicine; Business; Radiology; Economic growth; Economics; Environmental health; Government (linguistics)","routes":{"ca_aff":false,"ca_fund":true,"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":[0.002234101,0.001499922,0.000979605,0.01296858,0.003567213,0.005421629,0.003129785,0.001133654,0.04748693],"category_scores_gemma":[0.007697482,0.000999706,0.001539671,0.02065976,0.0007441523,0.001494375,0.002329464,0.002022511,0.01469044],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07156515,"about_ca_system_score_gemma":0.2293513,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.987296,"about_ca_topic_score_gemma":0.9879915,"domain_scores_codex":[0.9945297,0.0001701815,0.0002156922,0.0002175177,0.003917171,0.0009499025],"domain_scores_gemma":[0.9790601,0.0003788383,0.0004634809,0.0002775616,0.01726019,0.002559883],"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":[0.00006642071,0.00002076326,0.004513887,0.0004763136,0.00004452481,0.00006783313,0.00006163956,0.0008024151,0.0001487382,0.004666541,0.9358109,0.05332004],"study_design_scores_gemma":[0.00001671878,0.00001024584,0.01697356,0.0003043796,0.00002424797,0.0001030815,0.0001499181,0.000828408,0.0001355493,0.000444407,0.9809714,0.00003816791],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.006327457,0.01684236,0.003199834,0.01898388,0.002410938,0.0008651413,0.6179182,0.002713828,0.3307384],"genre_scores_gemma":[0.08271608,0.04375697,0.01920373,0.006532847,0.0008632804,0.001065094,0.6173519,0.001598354,0.2269118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9284348,"threshold_uncertainty_score":0.519244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02682907311153043,"score_gpt":0.3377059065997919,"score_spread":0.3108768334882615,"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."}}