{"id":"W4405790540","doi":"10.21203/rs.3.rs-5271440/v1","title":"Using Interpretable Survival Analysis to Assess Hospital Length of Stay","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistics; Medicine; Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006837483,0.000971903,0.0006030891,0.003750619,0.0002434862,0.002014772,0.0005470972,0.0008700843,0.004256753],"category_scores_gemma":[0.03882619,0.0001745631,0.001107173,0.002009506,0.0003795199,0.001300861,0.0006473122,0.001298352,0.0007115198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000523877,"about_ca_system_score_gemma":0.0007713366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001713223,"about_ca_topic_score_gemma":0.001740055,"domain_scores_codex":[0.9968195,0.002029927,0.000249646,0.0003095977,0.0004446473,0.0001465977],"domain_scores_gemma":[0.9605117,0.03071505,0.004060853,0.002322896,0.001948233,0.0004412689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002627027,0.000388256,0.6806049,0.0003199938,0.001402805,0.0005135669,0.0008858032,0.03086905,0.004334165,0.006285441,0.006856341,0.2649127],"study_design_scores_gemma":[0.0002904163,0.00238559,0.4573538,0.0003026554,0.001118389,0.001316509,0.001367569,0.4760742,0.00500403,0.04768196,0.006910986,0.000193895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7135876,0.001316764,0.2682271,0.001852312,0.0004162724,0.0001719419,0.00764843,0.001278734,0.005500742],"genre_scores_gemma":[0.9726887,0.0002243154,0.02286997,0.0001029977,0.0001818638,0.00009192189,0.002706236,0.0001209005,0.001013224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006837483,"threshold_uncertainty_score":0.03616053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1988737157253662,"score_gpt":0.5083349352500772,"score_spread":0.3094612195247111,"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."}}