{"id":"W2586242432","doi":"10.1371/journal.pone.0171759","title":"Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Women's College Hospital; University of Toronto","funders":"","keywords":"Fecal occult blood; Medicine; Colorectal cancer; Odds ratio; Internal medicine; Cancer; Colonoscopy; Population; Cancer screening; Anemia; Gastroenterology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005057002,0.0001267669,0.000749642,0.000388761,0.0003056595,0.00002086279,0.0001322777,0.00007538766,0.0000333731],"category_scores_gemma":[0.0003660179,0.0001184708,0.0001770035,0.0003633491,0.00004338995,0.00008326564,0.000106161,0.0001466054,0.000002312454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002187998,"about_ca_system_score_gemma":0.00003360527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004403125,"about_ca_topic_score_gemma":0.002719698,"domain_scores_codex":[0.9987057,0.00004503063,0.0003499779,0.0002655996,0.0004347775,0.000198895],"domain_scores_gemma":[0.9986612,0.0001197536,0.0006189684,0.0002960509,0.000209047,0.00009496471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003091112,0.0001539354,0.8656245,0.0008410992,0.003334798,0.000001011252,0.000892791,0.0004801679,0.1237271,0.000003007189,0.00000440997,0.001846086],"study_design_scores_gemma":[0.001450623,0.002128865,0.7563007,0.0009127925,0.00609231,0.000001298185,0.00007745319,0.07579423,0.157124,6.787827e-7,0.000008468083,0.0001085195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985477,0.0001490827,0.0002600937,0.00005451886,0.00005884194,0.0006272034,0.000160604,0.00006068193,0.00008131051],"genre_scores_gemma":[0.9982657,0.00004319773,0.001144718,0.000006471804,0.00008563256,0.000240448,0.00003524443,0.00002147766,0.0001570996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1093238,"threshold_uncertainty_score":0.6656238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04680718525868259,"score_gpt":0.2989582571656434,"score_spread":0.2521510719069608,"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."}}