{"id":"W4377194442","doi":"10.1177/08465371231176550","title":"Automated MRI Protocolling and Scheduling: A Multi-Institutional Survey and Results","year":2023,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fraser Health; University of British Columbia","funders":"","keywords":"Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.01319719,0.0003109788,0.0003925526,0.002454059,0.0005564624,0.001295281,0.0009681268,0.0004590761,0.001740567],"category_scores_gemma":[0.08243082,0.0003226004,0.0006007187,0.003473471,0.0005704013,0.001258617,0.001142906,0.0005224461,0.0006744887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334717,"about_ca_system_score_gemma":0.002604551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007381174,"about_ca_topic_score_gemma":0.008314092,"domain_scores_codex":[0.9899356,0.003930641,0.001870029,0.001268616,0.002429836,0.000565369],"domain_scores_gemma":[0.9113895,0.03747531,0.02589466,0.007106442,0.01582597,0.00230802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005113734,0.0003804117,0.9244341,0.0001943935,0.0001284344,0.0001048873,0.001508421,0.0004013421,0.0008712598,0.000116362,0.002297103,0.06905186],"study_design_scores_gemma":[0.00004223685,0.0005521029,0.9829726,0.0001865126,0.0002137886,0.000890056,0.003049041,0.001888486,0.001624287,0.0001580652,0.008372817,0.00005009068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915801,0.001065815,0.003086239,0.000353606,0.0000388369,0.0002431164,0.001260697,0.0001436606,0.002227985],"genre_scores_gemma":[0.9904778,0.001205611,0.004708514,0.0004308232,0.00003486307,0.0002224875,0.002127446,0.0001361415,0.0006563458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01319719,"threshold_uncertainty_score":0.0697943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05539424534405369,"score_gpt":0.3485258183734812,"score_spread":0.2931315730294276,"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."}}