{"id":"W4401596115","doi":"10.51731/cjht.2024.950","title":"Canadian Medical Imaging Inventory 2022–2023: CT","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Population; Computed tomography; Nuclear medicine; Environmental health; Geography; Demography; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133829,0.0001312279,0.0003285245,0.002181534,0.0002123047,0.00008649928,0.0002990075,0.0000893641,0.0007261081],"category_scores_gemma":[0.001210107,0.0001141636,0.0001073672,0.0006456072,0.0001811982,0.0002131497,0.00001279409,0.001085083,0.0000812045],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017582,"about_ca_system_score_gemma":0.03760693,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2487075,"about_ca_topic_score_gemma":0.2853521,"domain_scores_codex":[0.9982165,0.00004258924,0.0005742292,0.0001648208,0.0003586123,0.0006432549],"domain_scores_gemma":[0.9986971,0.00003952528,0.0001136939,0.0002128793,0.0001022652,0.0008345296],"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":[0.000001520645,0.000003460052,0.01302905,0.0001270931,0.00004606513,0.006520439,0.000151242,0.000001408081,0.000008500762,0.0008816325,0.1547126,0.824517],"study_design_scores_gemma":[0.0002255011,0.00006436522,0.003093483,0.00103718,0.00001980637,0.00580216,0.002148672,0.000865821,0.00008970794,0.0008588757,0.9856829,0.0001115018],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04207418,0.3743483,0.0001179486,0.5770578,0.002252797,0.0002099033,0.0000206927,0.0002090996,0.003709277],"genre_scores_gemma":[0.9914428,0.0015709,0.00007811486,0.00634146,0.00009199516,0.000003275827,0.000004765903,0.00002435831,0.0004423263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9493686,"threshold_uncertainty_score":0.967849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575142923157614,"score_gpt":0.3011301623496166,"score_spread":0.2853787331180405,"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."}}