{"id":"W4390114803","doi":"10.2196/54952","title":"Correction: Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Health records; Electronic health record; Nursing homes; Nursing; Nursing records; Medicine; Data science; Gerontology; Psychology; MEDLINE; Computer science; Health care; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001420022,0.0001812169,0.0005339634,0.0002012324,0.0008716997,0.00001972022,0.0001810764,0.00009657331,0.00007463612],"category_scores_gemma":[0.0001765057,0.000179639,0.00004691686,0.0009186012,0.00005578113,0.0003551866,0.0001410257,0.0005275682,0.000008116263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009776,"about_ca_system_score_gemma":0.00132636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002367653,"about_ca_topic_score_gemma":0.00005763604,"domain_scores_codex":[0.9973283,0.000467931,0.0007618982,0.0004779783,0.0002877584,0.0006761314],"domain_scores_gemma":[0.9983647,0.0003730559,0.0005749466,0.000419955,0.00008935321,0.0001779421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001117437,0.0001351578,0.02697141,0.03531174,0.0001018727,0.000009480797,0.0208647,0.000005539177,0.001319828,0.00002841714,0.7858094,0.1293307],"study_design_scores_gemma":[0.001798487,0.0006392405,0.02058285,0.9274287,0.0003075916,0.00007408598,0.03679346,0.003326915,0.0002900089,0.0002704099,0.007557751,0.0009305452],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6734601,0.2779036,0.003822949,0.00978351,0.02209523,0.009377613,0.00022711,0.001360499,0.001969429],"genre_scores_gemma":[0.9149613,0.07883199,0.0004382812,0.0006431348,0.001344724,0.0001625957,0.0009711216,0.0001431871,0.002503694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8921169,"threshold_uncertainty_score":0.7325465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1667749924413437,"score_gpt":0.5210743416234075,"score_spread":0.3542993491820638,"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."}}