{"id":"W1168915167","doi":"10.1007/s10278-015-9796-2","title":"A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women","year":2015,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Health Sciences Centre; North York General Hospital; Sunnybrook Health Science Centre","funders":"Canadian Breast Cancer Research Alliance","keywords":"False positive paradox; Receiver operating characteristic; Computer science; Metric (unit); Magnetic resonance imaging; Breast cancer; False positives and false negatives; Artificial intelligence; Computer-aided diagnosis; Pattern recognition (psychology); Mammography; Computer-aided; Cancer; Radiology; Medicine; Machine learning; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007511788,0.0001657987,0.0005193249,0.0006269104,0.00004473689,0.0000799539,0.0001903125,0.00002742737,0.000006107316],"category_scores_gemma":[0.0004032405,0.0001347834,0.0001344221,0.0007142881,0.0001163199,0.0007178435,0.00003051385,0.0002520064,1.687242e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004560057,"about_ca_system_score_gemma":0.000218315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006355976,"about_ca_topic_score_gemma":0.0000413543,"domain_scores_codex":[0.9981565,0.0001023124,0.000791327,0.0002039676,0.0004801745,0.0002657557],"domain_scores_gemma":[0.996955,0.001044635,0.0008964963,0.0001875849,0.0008069067,0.0001093459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008913994,0.0003532715,0.1109492,0.00009345595,0.00007758183,0.00002984597,0.002958971,0.002822078,0.02538794,0.000002875643,0.00006055975,0.8563728],"study_design_scores_gemma":[0.008431299,0.0006231269,0.8555126,0.002524929,0.0002948718,0.0001382822,0.003672909,0.1053793,0.02274372,0.0004399394,0.00003566559,0.000203347],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539585,0.004174106,0.1398823,0.001089054,0.0001796064,0.0003527521,0.000326397,0.000009490015,0.00002780072],"genre_scores_gemma":[0.9927163,0.00009072318,0.006866164,0.00009894474,0.0001509078,0.0000387548,0.000007102752,0.00002737683,0.000003793639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8561695,"threshold_uncertainty_score":0.5496309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233405828279928,"score_gpt":0.2757025409793961,"score_spread":0.2633684826965968,"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."}}