{"id":"W33784071","doi":"10.1021/acs.analchem.1c00253","title":"Data and Information Quality at the Canadian Institute for Health Information.","year":2006,"lang":"en","type":"article","venue":"ICIQ","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Mental Health; NIH Clinical Center; National Institute on Aging","keywords":"Health information; Quality (philosophy); Data quality; Information quality; Computer science; Information system; Political science; Business; Health care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00987446,0.001469827,0.002608254,0.007352965,0.003052038,0.006226344,0.004082011,0.002637715,0.5031888],"category_scores_gemma":[0.06954569,0.0008531737,0.001056787,0.01324695,0.001207951,0.002202726,0.003399929,0.002219128,0.1688708],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01727173,"about_ca_system_score_gemma":0.06536067,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.708871,"about_ca_topic_score_gemma":0.5937559,"domain_scores_codex":[0.9884182,0.001382718,0.001383392,0.001514114,0.006138532,0.001163107],"domain_scores_gemma":[0.9507404,0.005492438,0.001126186,0.006380999,0.03380869,0.002451257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001230612,0.00002905786,0.001572157,0.0005908678,0.00003821533,0.00003873341,0.0000830401,0.0001384896,0.0001354598,0.005180685,0.9410144,0.05105589],"study_design_scores_gemma":[0.00004952439,0.00001583639,0.004812373,0.0006787828,0.00003856937,0.000032628,0.0001239086,0.0005181142,0.0002552868,0.003025718,0.9904025,0.00004688781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0004414495,0.001186227,0.003272526,0.01175298,0.002203759,0.000601718,0.8525282,0.002993628,0.1250194],"genre_scores_gemma":[0.0208789,0.004565762,0.02216865,0.006567883,0.0004103735,0.00138598,0.7727748,0.001830995,0.1694167],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9827282,"threshold_uncertainty_score":0.7086408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3537222237963629,"score_gpt":0.5174123702046263,"score_spread":0.1636901464082633,"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."}}