{"id":"W3127166591","doi":"10.2196/23934","title":"Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Alberta Health Services","funders":"","keywords":"Medicine; MEDLINE; Comorbidity; Systematic review; Medical record; Medical emergency; Family medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00245711,0.0001730973,0.0003493422,0.0000554593,0.0004465156,0.0001119846,0.0009865413,0.0001943961,0.0008734882],"category_scores_gemma":[0.004944263,0.000124714,0.000137874,0.0005342491,0.00009816627,0.0003254573,0.0002729336,0.0009992619,0.00006229989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001066644,"about_ca_system_score_gemma":0.003454639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002067422,"about_ca_topic_score_gemma":0.0001340439,"domain_scores_codex":[0.9967763,0.0002197622,0.001008106,0.0001923442,0.001207998,0.0005954714],"domain_scores_gemma":[0.9958662,0.002400583,0.0002970196,0.0007569004,0.0002714155,0.0004078941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009348407,0.0001481096,0.0001126523,0.04323647,0.00008082152,0.0007787073,0.002540853,0.00006401506,6.889511e-7,0.08208412,0.02396985,0.8469744],"study_design_scores_gemma":[0.0005902586,0.00007862678,0.00001061212,0.02961169,0.00002079448,0.001853427,0.0001590434,0.942877,0.00001327652,0.0005573024,0.02401528,0.0002126989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.0005794963,0.01359514,0.9156747,0.06664341,0.00073076,0.001866678,0.000006695345,0.0003091712,0.0005939619],"genre_scores_gemma":[0.119912,0.04200229,0.1578254,0.6680388,0.003030296,0.008016288,0.0005499476,0.0001730879,0.0004518503],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.942813,"threshold_uncertainty_score":0.9564083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03062396636543229,"score_gpt":0.3893420425095209,"score_spread":0.3587180761440886,"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."}}