{"id":"W7154167045","doi":"10.2196/preprints.83244","title":"Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability (Preprint)","year":2025,"lang":"","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Receiver operating characteristic; Psychological intervention; Cluster analysis; Boosting (machine learning); Predictive analytics; Health care; Gradient boosting; Ceteris paribus","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.01524368,0.0005176204,0.0006124876,0.0003147382,0.001775233,0.0009753195,0.0007896002,0.0002249965,0.00002004743],"category_scores_gemma":[0.01074772,0.0005111865,0.00009397789,0.0005319387,0.0001160888,0.001300132,0.004521523,0.0005161682,8.020994e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006336692,"about_ca_system_score_gemma":0.002060332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007294378,"about_ca_topic_score_gemma":0.001741143,"domain_scores_codex":[0.9934657,0.0009070922,0.001503833,0.002271213,0.000890299,0.0009619003],"domain_scores_gemma":[0.9928681,0.003659698,0.0005156294,0.001012789,0.001654785,0.0002889455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003603658,0.0003998346,0.6367946,0.001563907,0.00009559654,7.642763e-7,0.03677989,0.0002427735,0.000004666383,0.001252324,0.0000593291,0.3224459],"study_design_scores_gemma":[0.002944876,0.0007944259,0.4862192,0.0005422222,0.00006020159,0.000001218789,0.005532351,0.5018447,0.0001553178,0.001592111,0.00002524157,0.0002880784],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6445246,0.0002916523,0.3371459,0.003335449,0.0007027197,0.0137886,0.00001122721,0.000126408,0.00007340555],"genre_scores_gemma":[0.9532358,0.000008285216,0.04274216,0.0002583463,0.0000792913,0.003521214,0.00001430782,0.00002801062,0.0001125522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5016019,"threshold_uncertainty_score":0.999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02251281931125407,"score_gpt":0.3720834531456266,"score_spread":0.3495706338343725,"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."}}