2412-PUB: Demographics of Women with Gestational Diabetes Attending a Diabetes and Pregnancy Clinic: 2000-2002, 2010-2012, and 2014-2016
Bibliographic record
Abstract
Background: Numbers of women attending Diabetes and Pregnancy Clinics (DPC) are increasing. Potential reasons include: obesity; fertility intervention access; diagnostic changes for gestational diabetes (GDM). The DPC in London Ontario sees all pregnant women with diabetes within the catchment area with stable clinic structure and procedures. Longitudinal characteristics of GDM women were documented for demographic insights. Methods: DPC pregnancy charts were assessed for 2000-2002, 2010-2012 and 2014-2016. Data were abstracted for: age; weight, infertility interventions; GDM diagnostic method. Continuous results were analyzed by one-way analysis of variance (ANOVA), non-parametric results by Chi-square testing; p≤ 0.05 signifying significance. Conclusions: Over the study interval, women with a GDM diagnosis increased 240%. The change may be related to obesity; changes to GDM diagnostic criteria and week of testing; but not late pregnancy weight gain or use of fertility interventions. Disclosure P. Khanna: None. K. Anukam: None. L. Chow: None. E. Brydges: None. S.L. Liu: Consultant; Self; Novo Nordisk Inc., Sanofi. Research Support; Self; Physicians' Services Inc. Foundation. J. Mahon: None. T.R. Joy: Speaker's Bureau; Self; Amgen Inc., Novo Nordisk Inc., Sanofi. Other Relationship; Self; Amgen Inc., AstraZeneca, Novo Nordisk Inc. R.M. McManus: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".