{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01700653,0.0005708511,0.000692977,0.004862472,0.0005014242,0.00217301,0.001010995,0.0004855794,0.0008778345],"category_scores_gemma":[0.08741079,0.0003444554,0.0007464535,0.004108735,0.0004444922,0.00127519,0.001268032,0.0004991238,0.0008199516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006414179,"about_ca_system_score_gemma":0.001692182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005452079,"about_ca_topic_score_gemma":0.01028742,"domain_scores_codex":[0.9787701,0.009006616,0.004129149,0.002055679,0.005627146,0.0004113392],"domain_scores_gemma":[0.9224201,0.04125386,0.01816808,0.005662332,0.01213665,0.0003588678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001529463,0.0001845837,0.8830152,0.0003631742,0.0003404536,0.0001184084,0.0003651649,0.001852995,0.00166806,0.0004357693,0.002607757,0.1088954],"study_design_scores_gemma":[0.00007657879,0.0002746213,0.943875,0.0007013727,0.0003311349,0.0006080347,0.0007466675,0.03866023,0.005390011,0.001913548,0.007366168,0.00005662134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8247345,0.001792959,0.1414573,0.001622885,0.0002755606,0.002425838,0.01787701,0.001282571,0.008531248],"genre_scores_gemma":[0.8482046,0.0005780386,0.1381905,0.0004602231,0.0001350588,0.0005967917,0.01087928,0.00005893405,0.0008966632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01700653,"threshold_uncertainty_score":0.08994019,"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."}}