{"id":"W4416868033","doi":"10.1101/2025.11.26.25341040","title":"Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes","year":2025,"lang":"","type":"preprint","venue":"medRxiv","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Institute of Health Services and Policy Research","funders":"","keywords":"Deep learning; Type 2 diabetes; Prediabetes; Glycated hemoglobin; Convolutional neural network; Artificial neural network; Support vector machine; Feature engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004792946,0.0006452372,0.0009732073,0.0003588471,0.000527468,0.0002304699,0.002725338,0.0006063816,0.00003555885],"category_scores_gemma":[0.003457455,0.0004734834,0.0003956705,0.001276498,0.000359785,0.0001846156,0.00118,0.002389369,0.000008235695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000104524,"about_ca_system_score_gemma":0.0008910926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003282851,"about_ca_topic_score_gemma":0.00003328295,"domain_scores_codex":[0.99404,0.001046225,0.001782003,0.001466195,0.0008438032,0.0008218333],"domain_scores_gemma":[0.9901632,0.004974096,0.001411516,0.001888616,0.001337923,0.0002246622],"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":[0.0003500135,0.00006937503,0.8902627,0.001625586,0.0006626548,0.000002430743,0.001810808,0.04550748,0.00005251144,0.00034894,0.00003409719,0.05927337],"study_design_scores_gemma":[0.0005347435,0.002025167,0.3521431,0.0008874925,0.000171196,0.000001182549,0.00005833621,0.6411499,0.00005426573,0.0001367701,0.00256024,0.0002776172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6048597,0.001761762,0.3812979,0.00300481,0.005409558,0.002942678,0.00006674074,0.0003644453,0.0002924738],"genre_scores_gemma":[0.9647761,0.000316662,0.03347649,0.0001026963,0.0003512038,0.0002625964,0.00005380125,0.00005564148,0.0006048256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5956424,"threshold_uncertainty_score":0.9999121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264367787554617,"score_gpt":0.2950899944634397,"score_spread":0.268653215707978,"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."}}