{"id":"W1850048152","doi":"10.1186/s12911-015-0196-9","title":"Administrative health data in Canada: lessons from history","year":2015,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; M.S.I. Foundation","keywords":"Standardization; Health informatics; Terminology; Health policy; Government (linguistics); Data quality; Public health informatics; Public relations; Public health; HRHIS; Data science; Medicine; Political science; Business; Computer science; Nursing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.04500125,0.0005992176,0.0007365447,0.01005735,0.03664096,0.03257523,0.004102562,0.003752874,0.004571919],"category_scores_gemma":[0.08313663,0.001123248,0.0006105731,0.03449582,0.05153616,0.01533426,0.009083269,0.01025525,0.0004025649],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2695688,"about_ca_system_score_gemma":0.3186951,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9839292,"about_ca_topic_score_gemma":0.9840061,"domain_scores_codex":[0.9623921,0.01320918,0.001895717,0.002691244,0.01533672,0.004475099],"domain_scores_gemma":[0.853161,0.08627597,0.005225603,0.005877012,0.04007008,0.009390334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006845784,0.00003237478,0.01349583,0.00144012,0.00004722445,0.001363383,0.3747184,0.0007105582,0.0002332748,0.3715588,0.1040745,0.132257],"study_design_scores_gemma":[0.000005953778,0.00001526688,0.01262663,0.003136277,0.00002417011,0.00023929,0.1613304,0.0003390813,0.0001911569,0.02257251,0.7994258,0.00009340984],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05495687,0.09462424,0.01039805,0.7150069,0.002959422,0.0002559294,0.004124435,0.0001585274,0.1175156],"genre_scores_gemma":[0.7797174,0.133019,0.01479838,0.04930744,0.001515457,0.0002885597,0.002313152,0.0004283998,0.01861218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7304312,"threshold_uncertainty_score":0.8471966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7034630045609181,"score_gpt":0.5568255713376772,"score_spread":0.146637433223241,"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."}}