{"id":"W2890057771","doi":"10.23889/ijpds.v3i4.691","title":"Validating health conditions in a clinical registry using administrative data algorithms","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity Western University; University of Calgary; University of Manitoba","funders":"","keywords":"Medicine; Medical record; Population; Disease registry; Confidence interval; Medical prescription; Cohort; Diagnosis code; Emergency medicine; Physical therapy; Internal medicine; Disease; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"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":[0.1153758,0.001159592,0.001649966,0.008446203,0.0008889358,0.004660298,0.003206293,0.001290383,0.00169577],"category_scores_gemma":[0.2766536,0.0007955248,0.002000915,0.008717238,0.001349588,0.002198825,0.003200093,0.00106236,0.0009575429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002360941,"about_ca_system_score_gemma":0.006936125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02494069,"about_ca_topic_score_gemma":0.01488952,"domain_scores_codex":[0.8519571,0.08552776,0.02652092,0.01343811,0.02044033,0.002115761],"domain_scores_gemma":[0.6712319,0.1679789,0.0779767,0.02996568,0.05060663,0.002240089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002627333,0.000227318,0.9570789,0.0005329148,0.0008989781,0.00007289109,0.0002582161,0.007132212,0.0002959232,0.001435939,0.003406148,0.02839789],"study_design_scores_gemma":[0.0005134279,0.0005660902,0.8581421,0.001089962,0.0008244171,0.0005482179,0.0006835505,0.1147631,0.003717266,0.004549812,0.01447611,0.0001259816],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6950451,0.002226382,0.2210823,0.002306898,0.0004177235,0.009326581,0.05309659,0.001664423,0.01483399],"genre_scores_gemma":[0.7814187,0.0006750477,0.1719853,0.0007151645,0.0002342996,0.004525006,0.03971504,0.00009719848,0.0006342969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1153758,"threshold_uncertainty_score":0.6101727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.872403365205847,"score_gpt":0.675343346029787,"score_spread":0.1970600191760601,"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."}}