{"id":"W2942370392","doi":"10.1089/dia.2018.0387","title":"Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes","year":2019,"lang":"en","type":"article","venue":"Diabetes Technology & Therapeutics","topic":"Diabetes Management and Research","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; LMC Diabetes & Endocrinology (Canada)","funders":"Novo Nordisk; Sanofi; AstraZeneca","keywords":"Medicine; High-intensity interval training; Continuous glucose monitoring; Type 1 diabetes; Interval training; Internal medicine; Basal (medicine); Diabetes mellitus; Type 2 diabetes; Stage (stratigraphy); Intensity (physics); Endocrinology; Cardiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488188,0.0002280857,0.0007140153,0.000584046,0.00003810857,0.00001784263,0.0002215467,0.0002306097,0.00002835241],"category_scores_gemma":[0.00007364147,0.0001846794,0.00004364648,0.0006472705,0.0003963485,0.0001169922,0.000233448,0.0004958425,0.00002525371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004663288,"about_ca_system_score_gemma":0.0000415217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002950422,"about_ca_topic_score_gemma":0.000002478322,"domain_scores_codex":[0.9985,0.00003182867,0.0003033006,0.000361772,0.0002138114,0.0005892633],"domain_scores_gemma":[0.9990205,0.0001741211,0.0001375849,0.0004161454,0.0001957138,0.00005593434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002573419,0.0001066848,0.8183233,0.0003900766,0.0003504926,0.00001173107,0.0005197283,0.000002641199,0.1303625,0.00003343079,0.00000178726,0.04964022],"study_design_scores_gemma":[0.006275965,0.003267476,0.5025811,0.00378539,0.0002827533,0.000003643712,0.003170082,0.001600729,0.4777819,0.0004719422,0.0002895167,0.00048951],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969828,0.001465623,4.695239e-7,0.0005237176,0.0001301701,0.0005812825,0.000002820852,0.0001602668,0.0001529214],"genre_scores_gemma":[0.9989654,0.0002204872,0.0003799114,0.00009187992,0.00003770744,0.00001822485,0.00000956549,0.00004344293,0.0002333373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3474194,"threshold_uncertainty_score":0.7531007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224546918557935,"score_gpt":0.2531019672330308,"score_spread":0.2408564980474515,"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."}}