{"id":"W2969254250","doi":"10.2196/15799","title":"Correction: Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review","year":2019,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Breast cancer; Medical physics; Medicine; Computer science; Data science; Cancer; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01580512,0.003580012,0.003374465,0.00922197,0.003941346,0.006875193,0.00549538,0.0131247,0.0481933],"category_scores_gemma":[0.2163797,0.002030742,0.003844231,0.006367703,0.003769747,0.003932673,0.00385695,0.01528751,0.02674993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007068111,"about_ca_system_score_gemma":0.01652071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0316718,"about_ca_topic_score_gemma":0.03541953,"domain_scores_codex":[0.979186,0.004353685,0.005790253,0.001863101,0.007687408,0.001119567],"domain_scores_gemma":[0.8590238,0.04408677,0.007393748,0.005190706,0.08117602,0.003128938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00002364901,0.000003568091,0.00004144918,0.0005353913,0.0000210732,0.0001223286,0.00003213525,0.00002473222,0.00001836786,0.0003573512,0.9945761,0.004243803],"study_design_scores_gemma":[0.0000946915,0.00002798413,0.0006095006,0.004813185,0.000180586,0.0007517899,0.0001519369,0.0002954052,0.0002540115,0.0018977,0.990838,0.00008522077],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00005880902,0.002365735,0.0006035728,0.07199501,0.9221763,0.00005545902,0.001266554,0.0002915911,0.001186935],"genre_scores_gemma":[0.009590778,0.02779959,0.00898786,0.2646901,0.5861417,0.001146019,0.004486656,0.002127242,0.09503021],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.0481933,"threshold_uncertainty_score":0.1612227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176949105467257,"score_gpt":0.3718070149458919,"score_spread":0.3400375238912193,"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."}}