{"id":"W2949907709","doi":"10.48550/arxiv.1905.02552","title":"Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Diabetes Management and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Glycemic; Public health; Pregnancy; Psychological intervention; Medicine; Scale (ratio); Population; Environmental health; Type 2 diabetes; Diabetes mellitus; Risk analysis (engineering); Gerontology; Intensive care medicine; Nursing; Geography; 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":[0.001098966,0.0007432788,0.0009961923,0.000726892,0.000719532,0.001481132,0.001482825,0.002435863,0.006594146],"category_scores_gemma":[0.004564348,0.0006415469,0.001565487,0.0009123267,0.0007499834,0.0008286147,0.001404095,0.001692067,0.0004531425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001927417,"about_ca_system_score_gemma":0.00219472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05704851,"about_ca_topic_score_gemma":0.0248587,"domain_scores_codex":[0.9995013,0.0002544538,0.0000230805,0.00007605689,0.00005894431,0.00008602149],"domain_scores_gemma":[0.9971786,0.002054923,0.0001924698,0.00009999095,0.0002896095,0.0001844726],"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":[0.00001552827,0.00001257561,0.0007356259,0.000009572413,0.00001205199,0.00002281529,0.00001503963,0.9955716,0.00005255011,0.002786702,0.0002184886,0.0005474419],"study_design_scores_gemma":[0.000009570662,0.000007858839,0.000186944,0.000005390669,0.000006209737,0.000004888659,0.00001570189,0.9976143,0.0000267193,0.00170168,0.0004161694,0.000004617757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4362756,0.001934335,0.4995542,0.005433496,0.0005329289,0.0003511286,0.007014888,0.001156226,0.04774713],"genre_scores_gemma":[0.9640892,0.0006274763,0.02388094,0.0002458982,0.00007032375,0.0003686142,0.001248446,0.0001126431,0.00935638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05704851,"threshold_uncertainty_score":0.1134329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1924496798447782,"score_gpt":0.2856554435301564,"score_spread":0.09320576368537814,"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."}}