{"id":"W4406760851","doi":"10.2196/59308","title":"Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Diabetes Management and Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Glycemic; Postprandial; Type 2 diabetes; Cohort; Environmental health; Cohort study; Gerontology; Demography; Diabetes mellitus; Internal medicine; Endocrinology","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":[0.02284384,0.002871098,0.00304921,0.001868061,0.004036417,0.002093426,0.002680445,0.002508117,0.02925907],"category_scores_gemma":[0.01700865,0.0019762,0.003529217,0.002429753,0.001608017,0.00167251,0.00194705,0.004650746,0.006660244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00398332,"about_ca_system_score_gemma":0.01732973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005068422,"about_ca_topic_score_gemma":0.008213581,"domain_scores_codex":[0.9903983,0.004500902,0.001954803,0.001127746,0.001068647,0.0009496263],"domain_scores_gemma":[0.9880276,0.00177385,0.001525966,0.002396759,0.005125171,0.001150663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.3312686,0.07489687,0.07361214,0.03632501,0.003676361,0.003973794,0.005800069,0.022881,0.0152557,0.01759509,0.1826687,0.2320467],"study_design_scores_gemma":[0.2337315,0.1033967,0.1876577,0.01862734,0.00263092,0.001211897,0.004892513,0.01774686,0.01139072,0.01295759,0.4044715,0.001284831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.004692677,0.0001460416,0.003140586,0.0002028022,0.000198372,0.9860765,0.004006333,0.00009974164,0.001436912],"genre_scores_gemma":[0.002046085,0.00007017116,0.003028959,0.00008666892,0.00001756239,0.993777,0.0006782695,0.000005418476,0.0002899118],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.02925907,"threshold_uncertainty_score":0.1208112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1299919447479164,"score_gpt":0.4818678735304136,"score_spread":0.3518759287824972,"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."}}