{"id":"W4408613433","doi":"10.2196/56671","title":"Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retrospective cohort study; Medicine; Computer science; Medical emergency; Emergency medicine; Machine learning; Nursing; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003703387,0.0006120562,0.0007465471,0.001604263,0.000457362,0.0008105684,0.0008551613,0.0006557766,0.0009588576],"category_scores_gemma":[0.01184138,0.0005284179,0.001496256,0.00150109,0.0002909944,0.0009685046,0.0007258664,0.001069408,0.0004200207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004299853,"about_ca_system_score_gemma":0.0007828403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00432147,"about_ca_topic_score_gemma":0.00409638,"domain_scores_codex":[0.9977848,0.0008251601,0.0003253033,0.0004495191,0.0004398215,0.0001754229],"domain_scores_gemma":[0.9927089,0.002976664,0.001411825,0.001169992,0.001343681,0.0003889549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001103821,0.0001621071,0.9960402,0.00002832121,0.0001025911,0.0002011396,0.00005735721,0.0004530376,0.0000473339,0.00002098217,0.0002139305,0.002562643],"study_design_scores_gemma":[0.00005682627,0.001430175,0.9447938,0.0001390389,0.0005595331,0.002371592,0.001206342,0.04744873,0.0004644042,0.0002630104,0.00121219,0.00005423465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969037,0.0002605477,0.002047084,0.00004085926,0.00001354122,0.00003994696,0.0005341771,0.00001055132,0.000149582],"genre_scores_gemma":[0.9963987,0.0002288463,0.001309717,0.00003889417,0.00002105237,0.00004663546,0.00183895,0.000008259152,0.0001090413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00432147,"threshold_uncertainty_score":0.01958561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867194839100044,"score_gpt":0.3185499146929642,"score_spread":0.2998779663019637,"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."}}