{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00331107,0.00007718276,0.0001346306,0.0001016416,0.003155801,0.00003797875,0.0001988567,0.0001248038,0.00006805972],"category_scores_gemma":[0.000840962,0.00005377806,0.00001243937,0.0001138472,0.00006767061,0.002580172,0.0001030127,0.000274674,0.0003232042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000474765,"about_ca_system_score_gemma":0.001826658,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3481999,"about_ca_topic_score_gemma":0.7789208,"domain_scores_codex":[0.998272,0.0001158718,0.0009011562,0.00006616685,0.0002305207,0.000414278],"domain_scores_gemma":[0.9985221,0.0002510212,0.000409098,0.0003976954,0.0001849106,0.0002351578],"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":[0.00003599828,0.000003584106,0.006398629,0.001061364,0.000004441381,2.792407e-8,0.004238644,0.00003869649,9.296993e-8,0.06409825,0.8950952,0.02902507],"study_design_scores_gemma":[0.0004975976,0.000017478,0.04766375,0.00004788157,0.000002826888,5.685947e-7,0.0004972967,0.00308353,2.505513e-7,0.0003999842,0.9477311,0.00005776046],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09287727,0.0006171777,0.05659011,0.6467487,0.007254164,0.0110065,0.01784813,0.0004820539,0.1665759],"genre_scores_gemma":[0.7601914,0.0001864191,0.002316443,0.2045896,0.00106084,0.0004255019,0.02973161,0.00001241076,0.001485779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6673142,"threshold_uncertainty_score":0.9981419,"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."}}