{"id":"W2980230325","doi":"10.1016/j.cjca.2019.07.077","title":"DERIVATION OF A MODEL THAT ACCURATELY PREDICTS CARDIOVASCULAR FROM NON-CARDIOVASCULAR CAUSE OF DEATH USING ADMINISTRATIVE HEALTH DATA SOURCES","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Medicine; Adjudication; Health care; Logistic regression; Record linkage; Population; Cause of death; Emergency medicine; Cardiovascular health; Clinical trial; Cohort; Health data; Medical emergency; Environmental health; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004195075,0.0001777712,0.001561329,0.000340754,0.0002597671,0.000006556931,0.0005417412,0.0003495208,0.00002965054],"category_scores_gemma":[0.0006512315,0.0001550856,0.0004147454,0.000176672,0.0001221311,0.0003670914,0.00006745203,0.0007430374,0.000007264096],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003369454,"about_ca_system_score_gemma":0.01216214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01154339,"about_ca_topic_score_gemma":0.0009939002,"domain_scores_codex":[0.9962399,0.0009870201,0.001452853,0.0002333217,0.0005340255,0.0005528927],"domain_scores_gemma":[0.9960542,0.0003515163,0.001307734,0.0009487795,0.0006080491,0.000729683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003892214,0.00001231641,0.4782846,0.002815139,0.008869826,0.00006708116,0.01918968,0.4704051,0.0001084275,0.0004913707,0.00793341,0.01143377],"study_design_scores_gemma":[0.01526858,0.002510939,0.5801193,0.008527939,0.00393132,0.0004060936,0.02763591,0.2307135,0.0003996,0.002087471,0.1270029,0.001396448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8497275,0.004497014,0.1418563,0.0003596986,0.001087998,0.00072567,0.0006626457,0.000006869112,0.001076346],"genre_scores_gemma":[0.9964586,0.0005228966,0.001996966,0.0003117849,0.000541679,0.000004288288,0.0001327342,0.00001957163,0.0000115061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2396916,"threshold_uncertainty_score":0.9950388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5952110671970376,"score_gpt":0.4587826386395379,"score_spread":0.1364284285574997,"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."}}