{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001165666,0.0002246112,0.0007320715,0.0007311309,0.0004939836,0.000008461074,0.0003262061,0.0003178843,0.001044649],"category_scores_gemma":[0.001125343,0.0002093862,0.0002941783,0.002577271,0.0001706969,0.0000729344,0.0001237159,0.0008117982,0.0001783679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855928,"about_ca_system_score_gemma":0.0005073976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004509347,"about_ca_topic_score_gemma":0.0004533277,"domain_scores_codex":[0.9965047,0.00117238,0.0009027933,0.0004037702,0.0002994074,0.0007169412],"domain_scores_gemma":[0.996423,0.001879094,0.0003457649,0.0006193428,0.0005881316,0.000144673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004339447,0.00004292822,0.9903685,0.0007089093,0.0004202478,6.499356e-7,0.0002529312,0.001103157,0.0005038572,0.00008881713,0.001917452,0.004549138],"study_design_scores_gemma":[0.0004652932,0.0005669023,0.790619,0.001004178,0.001061335,5.366346e-9,0.002212241,0.1248623,0.009311479,0.00068163,0.06861107,0.00060455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920069,0.001793564,0.00003698299,0.0006403205,0.0006241527,0.0008587349,0.00005228337,0.0001745189,0.003812537],"genre_scores_gemma":[0.9966328,0.00002317856,0.0001268799,0.001670834,0.0000924617,0.000294962,0.00006700973,0.000028911,0.001063007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1997495,"threshold_uncertainty_score":0.9998685,"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."}}