{"id":"W4388460768","doi":"10.1177/08465371231209255","title":"Clinical Impact of Preoperative Magnetic Resonance Imaging in Breast Cancer","year":2023,"lang":"en","type":"letter","venue":"Canadian Association of Radiologists Journal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; McMaster University","funders":"","keywords":"Medicine; Magnetic resonance imaging; Breast cancer; Radiology; Mammography; Breast MRI; Medical physics; Cancer; Internal 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":[0.001361574,0.0002459154,0.0006474594,0.0009053638,0.001670018,0.001877924,0.0007546555,0.01188944,0.004494448],"category_scores_gemma":[0.02086206,0.0003262249,0.0005618061,0.0009906386,0.001339664,0.001099826,0.0006576395,0.008316321,0.001601682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003692344,"about_ca_system_score_gemma":0.003608057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007422834,"about_ca_topic_score_gemma":0.02286952,"domain_scores_codex":[0.9984445,0.0004871961,0.0002563255,0.00009640436,0.0004091857,0.0003063119],"domain_scores_gemma":[0.9909966,0.004488885,0.0006428857,0.0002253886,0.001643307,0.002002944],"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":[0.0008480306,0.0002960302,0.08777301,0.0003934527,0.00008045714,0.1970636,0.0005564677,0.0008772176,0.002289656,0.004349922,0.6154485,0.09002365],"study_design_scores_gemma":[0.0003740063,0.0008551739,0.1134655,0.001677557,0.0003409305,0.3206753,0.00446504,0.004410328,0.002092406,0.01674638,0.5346311,0.000266346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02084847,0.006399974,0.0002314661,0.9440395,0.009582065,0.00001743409,0.0001309157,0.00003240256,0.01871773],"genre_scores_gemma":[0.3935959,0.01181789,0.001073865,0.4314249,0.151958,0.00004603258,0.0003854806,0.00007012841,0.009627799],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01188944,"threshold_uncertainty_score":0.02678996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679491037854059,"score_gpt":0.3438201489445286,"score_spread":0.327025238565988,"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."}}