{"id":"W4385575760","doi":"10.1002/osp4.705","title":"Automated extraction of weight, height, and obesity in electronic medical records are highly valid","year":2023,"lang":"en","type":"article","venue":"Obesity Science & Practice","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"","keywords":"Medicine; Medical record; Obesity; Electronic medical record; Incidence (geometry); Predictive value; Diagnosis code; Coding (social sciences); Cohort; Health records; Pediatrics; Health care; Family medicine; Internal medicine; Statistics; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01598573,0.000131518,0.0003013432,0.0004665508,0.001132401,0.00002129841,0.0003300714,0.0002964741,0.0002095071],"category_scores_gemma":[0.01455382,0.0001151317,0.00002496231,0.00219577,0.0003029329,0.001778652,0.0001776263,0.001380879,0.0004196755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004370595,"about_ca_system_score_gemma":0.002646507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001457242,"about_ca_topic_score_gemma":0.0008092372,"domain_scores_codex":[0.9957369,0.0007756919,0.0008166851,0.00036213,0.00142109,0.0008874981],"domain_scores_gemma":[0.9958606,0.002316135,0.0007212315,0.000295458,0.0003841051,0.0004224115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005396788,0.000886145,0.9083343,0.001987936,0.00002480654,0.0001107306,0.01313194,0.00003434754,0.001531434,0.0310167,0.02893776,0.0134643],"study_design_scores_gemma":[0.0008516726,0.0001743579,0.8990284,0.0005385692,0.00002124285,0.0000292185,0.003036476,0.04511791,0.0001507331,0.0008355853,0.05002786,0.0001879328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777189,0.0000832115,0.0002938137,0.01207634,0.0008365058,0.0004736721,0.000004249814,0.0004195278,0.008093811],"genre_scores_gemma":[0.9956184,0.001979556,0.0003762861,0.001486811,0.0001063369,0.00003258709,0.000009790474,0.000008234093,0.0003820203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04508356,"threshold_uncertainty_score":0.993747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1047011951294988,"score_gpt":0.472949443155938,"score_spread":0.3682482480264391,"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."}}