{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001766524,0.00137563,0.00106556,0.009651465,0.003967526,0.005879477,0.00385233,0.00163769,0.1408142],"category_scores_gemma":[0.01496136,0.0009695256,0.001395042,0.01551993,0.0008812789,0.00165658,0.002061085,0.002402541,0.03837061],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04822363,"about_ca_system_score_gemma":0.1731124,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9545047,"about_ca_topic_score_gemma":0.9650097,"domain_scores_codex":[0.9929754,0.0002663281,0.0002837813,0.0002918121,0.005373746,0.0008090855],"domain_scores_gemma":[0.9619905,0.0008819574,0.0009500092,0.0006178191,0.03154506,0.004014607],"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":[0.00002422125,0.0000136295,0.001537597,0.0001542273,0.00001389423,0.00002790874,0.00001593945,0.0001000749,0.00004043262,0.002079226,0.9673362,0.02865672],"study_design_scores_gemma":[0.00001421788,0.000006605405,0.006141829,0.0002299664,0.00001276883,0.00008596012,0.00004536477,0.0002741029,0.00006287999,0.000362508,0.9927438,0.00002004222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002459244,0.0104591,0.002039325,0.02511774,0.004331719,0.0007710287,0.4487223,0.00256204,0.5035374],"genre_scores_gemma":[0.04552753,0.03157967,0.01704357,0.01484727,0.00254872,0.001033196,0.5991581,0.001711273,0.2865507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9517764,"threshold_uncertainty_score":0.4710705,"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."}}