{"id":"W4288432333","doi":"10.3390/curroncol29080424","title":"Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation","year":2022,"lang":"en","type":"article","venue":"Current Oncology","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; McMaster University; University of Toronto","funders":"Cancer Care Ontario","keywords":"Medicine; Breast cancer; Algorithm; Cancer; Kappa; Cancer registry; Population; Cohort; Health care; Retrospective cohort study; Cohen's kappa; Internal medicine; Machine learning; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0465784,0.001028555,0.001500786,0.004711918,0.000894868,0.002424154,0.00227904,0.001055733,0.0009772535],"category_scores_gemma":[0.1160723,0.0007208366,0.001489205,0.004269029,0.0005065302,0.001214556,0.001576607,0.001138683,0.0003238058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002743355,"about_ca_system_score_gemma":0.008342793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0364149,"about_ca_topic_score_gemma":0.03729401,"domain_scores_codex":[0.9765018,0.01232917,0.004309939,0.002654219,0.003652913,0.0005520426],"domain_scores_gemma":[0.9141846,0.05380788,0.007038346,0.004402926,0.02005696,0.0005093006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001035902,0.00101861,0.6983534,0.001015944,0.001797086,0.0002387866,0.0007955917,0.05001857,0.00174114,0.001944298,0.00937703,0.2326636],"study_design_scores_gemma":[0.0009431288,0.000559649,0.2137126,0.0004927255,0.0007983889,0.0006343386,0.0004173227,0.7649211,0.005698626,0.00326547,0.008424995,0.000131606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.532874,0.001622671,0.4415967,0.001601098,0.0002011094,0.008202396,0.007957962,0.002463945,0.003480079],"genre_scores_gemma":[0.4232533,0.0003619737,0.566286,0.0002420507,0.00005743123,0.003065392,0.006239148,0.0001005898,0.0003941742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0465784,"threshold_uncertainty_score":0.246333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4861009274785509,"score_gpt":0.5216526901039741,"score_spread":0.03555176262542314,"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."}}