{"id":"W4381715312","doi":"10.1177/19322968231181138","title":"Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Diabetes Science and Technology","topic":"Diabetes Management and Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Montreal Children's Hospital","funders":"U.S. National Library of Medicine","keywords":"Continuous glucose monitoring; Series (stratigraphy); Computer science; Blood Glucose Self-Monitoring; Time series; Medicine; Type 1 diabetes; Diabetes mellitus; Machine learning; Biology; Endocrinology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002736713,0.0008379778,0.0005247396,0.0004428694,0.0002599482,0.000814339,0.00127551,0.001211113,0.001089535],"category_scores_gemma":[0.01537599,0.0003891705,0.0008582302,0.0005658932,0.0008114332,0.0009786311,0.000845038,0.00191067,0.0002218794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008728331,"about_ca_system_score_gemma":0.0008057815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006987,"about_ca_topic_score_gemma":0.005696039,"domain_scores_codex":[0.9991953,0.0004415619,0.00004628997,0.0001480447,0.0001090955,0.00005970064],"domain_scores_gemma":[0.9904093,0.00748857,0.0005766758,0.0008167272,0.0005234911,0.0001852886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001157198,0.00005980791,0.005579823,0.00004069477,0.00003086397,0.00004426358,0.0000380591,0.9855697,0.000346349,0.001801168,0.0005626407,0.005810969],"study_design_scores_gemma":[0.000008322623,0.00001795913,0.0004874217,0.00000639064,0.000004630585,0.000008685231,0.000005677934,0.9975275,0.0003839815,0.001350513,0.0001940156,0.000004951112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5212824,0.0006155028,0.4679777,0.002009374,0.0003415332,0.0002057592,0.00198265,0.0019653,0.003619893],"genre_scores_gemma":[0.9588824,0.000158319,0.03871531,0.0002494025,0.00006167072,0.0001176258,0.001230774,0.00004061995,0.0005438306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01006987,"threshold_uncertainty_score":0.02002251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02173282048765548,"score_gpt":0.3232555932649183,"score_spread":0.3015227727772629,"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."}}