{"id":"W6999633118","doi":"","title":"Creation and implementation of an electronic health record note for quality improvement in pediatric epilepsy: Practical considerations and lessons learned.","year":2021,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Documentation; Electronic health record; Quality management; Quality (philosophy); Construct (python library); Patient record; Health care; MEDLINE; Patient satisfaction; Health records","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05658305,0.0008862333,0.0003667858,0.001310734,0.001450028,0.005143358,0.004238992,0.001805132,0.004029362],"category_scores_gemma":[0.08573443,0.0004162893,0.0008955056,0.001467301,0.0009321847,0.005986779,0.003915856,0.003200725,0.0009330439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003295824,"about_ca_system_score_gemma":0.01674657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01155293,"about_ca_topic_score_gemma":0.01993988,"domain_scores_codex":[0.9668342,0.01766319,0.003254396,0.001567308,0.008981777,0.001699103],"domain_scores_gemma":[0.9238197,0.02893604,0.006754008,0.01404476,0.0195095,0.006936042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000163383,0.0007703439,0.02715617,0.0009970327,0.00007326058,0.0004617624,0.002499238,0.001534599,0.002121756,0.003053774,0.03494244,0.9262263],"study_design_scores_gemma":[0.001473229,0.008914747,0.1953389,0.01295648,0.0005099399,0.0058921,0.03390925,0.03025323,0.03078246,0.01784853,0.6614043,0.0007169327],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1997989,0.01243146,0.4945922,0.2353016,0.004783914,0.01561246,0.002315731,0.007420174,0.0277435],"genre_scores_gemma":[0.1433677,0.003862198,0.8401314,0.004476075,0.0007911817,0.002583315,0.001621002,0.0002298694,0.002937153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05658305,"threshold_uncertainty_score":0.2992433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0741568881509903,"score_gpt":0.4507498565703151,"score_spread":0.3765929684193248,"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."}}