{"id":"W3042549515","doi":"10.3747/co.27.5861","title":"Validation in Alberta of an Administrative Data Algorithm to Identify Cancer Recurrence","year":2020,"lang":"en","type":"article","venue":"Current Oncology","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Alberta Cancer Foundation; University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Confidence interval; Medical diagnosis; Chart; Kappa; Gold standard (test); Algorithm; Cancer registry; Cancer; Population; Predictive value; Diagnosis code; Gynecology; Obstetrics; Pediatrics; Internal medicine; Statistics; Radiology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0190038,0.0006809679,0.0007736124,0.005369065,0.001052783,0.001951984,0.002466145,0.0006687808,0.001698483],"category_scores_gemma":[0.06867848,0.0004834127,0.001212859,0.004353419,0.0006812023,0.0006415396,0.001623273,0.0009520692,0.0005139455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007017336,"about_ca_system_score_gemma":0.01152299,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5246209,"about_ca_topic_score_gemma":0.547062,"domain_scores_codex":[0.9911343,0.003280358,0.001030402,0.001276779,0.002655511,0.0006226132],"domain_scores_gemma":[0.9722916,0.01142353,0.00379249,0.001999903,0.009798242,0.0006942669],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003989174,0.00009214174,0.9733,0.00007247049,0.0002232095,0.00006304132,0.0001794127,0.003872968,0.0001215897,0.000475112,0.004203449,0.01699772],"study_design_scores_gemma":[0.0003801059,0.0002335126,0.8768339,0.0002573091,0.0003469178,0.0003830488,0.0007014563,0.1107483,0.0008586552,0.001272675,0.007931843,0.00005234355],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9207315,0.0013157,0.03502112,0.001586388,0.0001838091,0.002358006,0.02104458,0.0009895442,0.01676932],"genre_scores_gemma":[0.9515801,0.0002882442,0.03131054,0.0002865543,0.00003434294,0.0006736123,0.01457254,0.00006811351,0.001185904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9809962,"threshold_uncertainty_score":0.9563574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3854866846419229,"score_gpt":0.5537502628447267,"score_spread":0.1682635782028038,"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."}}