{"id":"W2897668054","doi":"10.2196/10933","title":"Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical record; Coding (social sciences); Health care; Health records; Data science; Medical classification; Electronic medical record; Clinical decision support system; Health information technology; Medical research; Electronic health record; Computer science; Knowledge management; Decision support system; Medicine; Nursing; Internet privacy; Data mining; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1980085,0.001431375,0.001140847,0.009022773,0.001749347,0.003460708,0.003055898,0.001084209,0.001938779],"category_scores_gemma":[0.2764811,0.0008002284,0.002305405,0.008679349,0.002352824,0.002450592,0.004607321,0.001339089,0.001506229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002914724,"about_ca_system_score_gemma":0.01555662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005418012,"about_ca_topic_score_gemma":0.004818022,"domain_scores_codex":[0.8003185,0.1281913,0.03368076,0.009493602,0.02655079,0.001765048],"domain_scores_gemma":[0.6584893,0.1561248,0.04463064,0.06305499,0.07523102,0.002469204],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001832818,0.002290287,0.6856602,0.002209336,0.0007600332,0.0002451816,0.006514193,0.004805074,0.004389519,0.003358229,0.00426417,0.283671],"study_design_scores_gemma":[0.001797541,0.005611182,0.8813565,0.002836226,0.0009097083,0.000778943,0.006128468,0.05014981,0.01830871,0.003856299,0.02804842,0.0002181511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5367345,0.00117622,0.319264,0.001539331,0.0003657785,0.1200819,0.01334394,0.001355837,0.006138457],"genre_scores_gemma":[0.3758034,0.0008082377,0.5617853,0.0006173943,0.0002685189,0.04393244,0.01594644,0.000141745,0.000696582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8019915,"threshold_uncertainty_score":0.9889984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3222300277310027,"score_gpt":0.651980294428436,"score_spread":0.3297502666974332,"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."}}