{"id":"W3158676142","doi":"10.1186/s12885-021-08526-9","title":"Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province","year":2021,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"CancerCare Manitoba Foundation","keywords":"Brier score; Algorithm; Medicine; Breast cancer; Cohort; Surgical oncology; Machine learning; Cancer; Data mining; Artificial intelligence; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.02137043,0.001020053,0.0008960907,0.002460518,0.001336702,0.001723296,0.002674749,0.0005861206,0.001064088],"category_scores_gemma":[0.06354502,0.0004756339,0.001466613,0.002132951,0.0005304914,0.0006531411,0.001122256,0.0008532063,0.0002235595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02109338,"about_ca_system_score_gemma":0.04260992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9074217,"about_ca_topic_score_gemma":0.9013683,"domain_scores_codex":[0.9918522,0.003308547,0.0009492345,0.00134525,0.001907475,0.000637236],"domain_scores_gemma":[0.963198,0.01617043,0.002635038,0.001129627,0.01604252,0.0008243379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001856382,0.0003250566,0.7801043,0.0004905008,0.001248598,0.0001268746,0.0005811053,0.07663063,0.000838983,0.001377981,0.00732686,0.1290927],"study_design_scores_gemma":[0.0007049093,0.0004943443,0.2943768,0.0002548069,0.0009388091,0.0002435027,0.0007496307,0.6936795,0.002584032,0.001135472,0.00474662,0.00009154088],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9180365,0.001917337,0.05968136,0.0018593,0.0001053315,0.002357474,0.008797551,0.001379038,0.005866132],"genre_scores_gemma":[0.8808985,0.0004861124,0.1100921,0.0003476612,0.00002324902,0.0004704594,0.006799762,0.00006539585,0.0008167301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09257829,"threshold_uncertainty_score":0.1862469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2627258816494955,"score_gpt":0.4692035081583036,"score_spread":0.2064776265088082,"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."}}