{"id":"W4403758117","doi":"10.3122/jabfm.2023.230381r1","title":"Using Primary Health Care Electronic Medical Records to Predict Hospitalizations, Emergency Department Visits, and Mortality: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"The Journal of the American Board of Family Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"University of Toronto; Canadian Institutes of Health Research; Government of Ontario","keywords":"Emergency department; Medicine; Health records; Primary care; Emergency medicine; Medical emergency; Medical record; Electronic health record; Health care; Family medicine; Nursing; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005919217,0.0004882714,0.00448911,0.00031934,0.0001735111,0.00001721794,0.002890026,0.00006963302,0.000006221745],"category_scores_gemma":[0.001474173,0.0002194428,0.000479866,0.002060616,0.000218749,0.00008065901,0.0007294079,0.0012872,0.000002479163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008942158,"about_ca_system_score_gemma":0.003535726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006152433,"about_ca_topic_score_gemma":0.00004374439,"domain_scores_codex":[0.9901094,0.003598002,0.003201132,0.0003571076,0.002255245,0.0004790925],"domain_scores_gemma":[0.992335,0.0004162042,0.005049329,0.001421216,0.0003677476,0.0004105312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000006307645,0.00002396885,0.000125119,0.9043239,0.0007642792,0.00001882269,0.001192242,0.00001040929,1.352285e-7,0.0001384344,0.009396313,0.08400003],"study_design_scores_gemma":[0.0001309087,0.002065149,0.0003308014,0.9201609,0.007055709,0.001024012,0.0002446298,0.0002483034,2.552798e-8,0.00006700532,0.06838936,0.000283203],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002054739,0.9833531,0.005451681,0.007579815,0.00108586,0.002258679,0.00001316968,0.00003000133,0.00002220377],"genre_scores_gemma":[0.0001536733,0.9942825,0.0007544881,0.004356032,0.0003514714,0.00002904424,0.000004755724,0.00004904797,0.00001892977],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.08371682,"threshold_uncertainty_score":0.8948616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04812500724831532,"score_gpt":0.4161107367128867,"score_spread":0.3679857294645714,"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."}}