{"id":"W6939656318","doi":"10.6084/m9.figshare.15087554.v1","title":"Additional file 1 of Validity of an algorithm to identify cardiovascular deaths from administrative health records: a multi-database population-based cohort study","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Calgary; Institute for Clinical Evaluative Sciences; University of Toronto; Memorial University of Newfoundland; Sunnybrook Health Science Centre; Saskatchewan Health Quality Council; University of British Columbia; Jewish General Hospital; Institut National d'Excellence en Santé et en Services Sociaux; University of Manitoba","funders":"","keywords":"Predictive value; Cohort study; Cohort; Retrospective cohort study; Diagnosis code; Health data; Data collection; Data file","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001377464,0.0001326434,0.0004154799,0.00003368304,0.0000858256,0.00003177722,0.0001882728,0.00005163826,0.9537306],"category_scores_gemma":[0.00272738,0.0001218501,0.0001439292,0.0001434653,0.000006526271,0.0001406142,0.00009841172,0.00007680272,0.000215801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000050006,"about_ca_system_score_gemma":0.0005216391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009773619,"about_ca_topic_score_gemma":0.0007190476,"domain_scores_codex":[0.9982302,0.0003625127,0.0003839782,0.0004388801,0.000425257,0.0001591836],"domain_scores_gemma":[0.9983593,0.0004200755,0.0001853762,0.0005139281,0.0003764638,0.0001448972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003300436,0.0005328553,0.00008701607,0.0001511369,0.00009790083,0.00004433192,0.0002315966,0.00006192361,0.006187984,3.118931e-7,0.9892552,0.003316774],"study_design_scores_gemma":[0.001941314,0.001765396,0.4703977,0.005764623,0.0001748454,0.00001621187,0.001700761,0.001959373,0.411243,0.00008962738,0.1039053,0.001041921],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01913525,0.00005277559,0.00009266385,0.000006077147,0.00006205057,0.0005361505,0.9800794,0.00002434684,0.00001130882],"genre_scores_gemma":[0.06358277,2.5817e-7,0.03022815,0.0001062194,0.00007633381,0.0004415666,0.9055119,0.00001330147,0.00003947454],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9535148,"threshold_uncertainty_score":0.4968903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09439920499864807,"score_gpt":0.3395812612937134,"score_spread":0.2451820562950653,"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."}}