{"id":"W7117562630","doi":"10.2196/82084","title":"Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning–Based Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cardiorespiratory fitness; Generalizability theory; Feature (linguistics); Sample (material); Heartbeat; Autonomic nervous system","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":[0.003495324,0.0007421284,0.0007487843,0.0021902,0.0001746832,0.001369197,0.0005537856,0.0006116075,0.001030073],"category_scores_gemma":[0.006238855,0.0001241111,0.0007785611,0.001702085,0.0002280144,0.0004741362,0.0003818412,0.000949916,0.0004961251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004296145,"about_ca_system_score_gemma":0.0003781295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001390857,"about_ca_topic_score_gemma":0.001373522,"domain_scores_codex":[0.998894,0.0004919054,0.00009390471,0.000217017,0.0002584091,0.00004471967],"domain_scores_gemma":[0.9974459,0.001329681,0.0004741096,0.000213821,0.0004580236,0.00007853457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005855603,0.0004524536,0.4673868,0.0006293845,0.00148612,0.0001947248,0.0001090633,0.04300733,0.006341733,0.001783028,0.006188057,0.4718357],"study_design_scores_gemma":[0.00004180116,0.00060249,0.4970444,0.0005142515,0.0005721854,0.000662396,0.0001755004,0.4760839,0.004811032,0.01095168,0.008436996,0.0001033157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5826555,0.04950769,0.3411193,0.004355974,0.0007350284,0.0005894834,0.008715327,0.001564067,0.0107577],"genre_scores_gemma":[0.9388241,0.004789328,0.05181931,0.000424249,0.0004601227,0.0002295283,0.002462115,0.00004957229,0.0009417539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003495324,"threshold_uncertainty_score":0.01848525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05209929121312692,"score_gpt":0.4257491805515479,"score_spread":0.3736498893384209,"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."}}