{"id":"W4403231639","doi":"10.2196/62831","title":"Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Hyperglycemia and glycemic control in critically ill and hospitalized patients","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Wearable computer; Database; Computer science; Association (psychology); Data science; World Wide Web; Psychology; Embedded 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":[],"consensus_categories":[],"category_scores_codex":[0.0004947309,0.0001334994,0.0004321775,0.00008232163,0.00005302618,0.00005044993,0.0001760853,0.00009120967,0.0001472833],"category_scores_gemma":[0.0001813774,0.00009327374,0.00003330305,0.0002725946,0.00009491928,0.0003316323,0.0003024394,0.0001580309,0.000003783236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008577925,"about_ca_system_score_gemma":0.00004006514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001490734,"about_ca_topic_score_gemma":0.0001101402,"domain_scores_codex":[0.9986129,0.0001320275,0.0002326978,0.0005609999,0.0002706933,0.0001907113],"domain_scores_gemma":[0.9984473,0.0006768033,0.00004958983,0.0006357743,0.00007672559,0.0001138246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001016633,0.0005521149,0.9782706,0.0004903843,0.004471513,0.00003033566,0.00004899659,5.915458e-7,0.01159461,0.00001760406,0.001437016,0.002984507],"study_design_scores_gemma":[0.00297543,0.0005981327,0.9580272,0.0007406291,0.008901792,0.000001935655,0.00008258878,0.02644865,0.00033349,0.0002222739,0.001429343,0.0002384739],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806752,0.01045151,0.00004082671,0.000564146,0.00003188873,0.0001917326,0.007954157,0.00003314088,0.00005746504],"genre_scores_gemma":[0.980514,0.000901424,0.0008671397,0.0001401308,0.0001096226,0.00001638474,0.01738212,0.000009198126,0.00006000345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02644805,"threshold_uncertainty_score":0.3803593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413290741349566,"score_gpt":0.3065909086603636,"score_spread":0.265261834525407,"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."}}