{"id":"W4416355075","doi":"10.48550/arxiv.2511.12409","title":"Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpretability; Benchmark (surveying); Predictive power; Artificial neural network; Deep learning; Transparency (behavior); Survival analysis; Function (biology); Generalized additive model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008182458,0.000599617,0.0003980083,0.0004069611,0.0002771615,0.0006532485,0.0009687168,0.0007359589,0.001650524],"category_scores_gemma":[0.002996851,0.0002056951,0.0004796697,0.0003415764,0.0004491985,0.0003726379,0.0007416918,0.001098716,0.0001974386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003125584,"about_ca_system_score_gemma":0.002068618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2452584,"about_ca_topic_score_gemma":0.264159,"domain_scores_codex":[0.9998388,0.00004382676,0.000007507204,0.00004274281,0.00002429399,0.00004275761],"domain_scores_gemma":[0.9993722,0.0003406284,0.00007533404,0.00004125493,0.0001078585,0.00006287597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003830692,0.0001098361,0.0552256,0.00008864828,0.00009078297,0.0002617507,0.0002139997,0.8695558,0.0008301493,0.004818548,0.006585382,0.06183651],"study_design_scores_gemma":[0.0000132091,0.00001629379,0.003802982,0.00001230702,0.00001009689,0.00001802815,0.00002221829,0.993219,0.0001280714,0.002421716,0.0003290157,0.00000712163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8779666,0.001204628,0.105123,0.005707548,0.0000980822,0.00007545914,0.005168387,0.0008445169,0.003811707],"genre_scores_gemma":[0.9901248,0.0001669353,0.00607308,0.0001454232,0.00002661319,0.00002170179,0.00155821,0.0000202042,0.001863123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7547416,"threshold_uncertainty_score":0.4876617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06240685773094019,"score_gpt":0.3196883411223565,"score_spread":0.2572814833914163,"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."}}