{"id":"W1983791908","doi":"10.1111/1475-6773.12159","title":"Using Computer‐Extracted Data from Electronic Health Records to Measure the Quality of Adolescent Well‐Care","year":2014,"lang":"en","type":"article","venue":"Health Services Research","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Agency for Healthcare Research and Quality","keywords":"Data extraction; Documentation; Data collection; Workflow; Data quality; Observational study; Health care; Quality (philosophy); Data mining; Computer science; Electronic health record; Measure (data warehouse); Medicine; Data validation; MEDLINE; Database; Statistics; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10325,0.0006121808,0.0009906348,0.009037832,0.000729827,0.002800241,0.00122867,0.0006342306,0.001066117],"category_scores_gemma":[0.3738037,0.0005917441,0.001123627,0.009903084,0.001236456,0.003040525,0.002340547,0.000793108,0.0002813653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002672627,"about_ca_system_score_gemma":0.00481415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006436565,"about_ca_topic_score_gemma":0.007260237,"domain_scores_codex":[0.8313462,0.09410793,0.04081656,0.006971175,0.02540356,0.001354558],"domain_scores_gemma":[0.4221096,0.3256479,0.156341,0.02384034,0.0705892,0.001471866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003456277,0.0001615897,0.9604289,0.0008499199,0.0004870947,0.00003201632,0.002234549,0.000443075,0.000400268,0.0001797095,0.0007687482,0.03366853],"study_design_scores_gemma":[0.0001691423,0.0007625133,0.9837326,0.0009102703,0.0003543553,0.0001619614,0.002554163,0.004281081,0.003618463,0.0003766162,0.003011149,0.00006764216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568113,0.001650908,0.02777991,0.0009074089,0.0001003925,0.002639786,0.006976454,0.0001643889,0.002969448],"genre_scores_gemma":[0.9666181,0.0004787569,0.02808258,0.000213774,0.00006899449,0.001485414,0.002831565,0.00003171297,0.0001891031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.10325,"threshold_uncertainty_score":0.5460449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8541315930421336,"score_gpt":0.7556544821754075,"score_spread":0.09847711086672606,"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."}}