Relationship between the adoption of preventive practices and the metabolic profile of women with prior gestational diabetes mellitus
Bibliographic record
Abstract
Women with prior gestational diabetes mellitus (GDM) are encouraged to adopt healthy lifestyle behaviours to prevent or delay type 2 diabetes. The objective was to examine the association between the adoption of preventive practices and the metabolic profile of women with prior GDM. Analyses included 181 women who had GDM between 2003 and 2010. The preventive practices examined included (i) regular physical activity (≥150 min·week(-1)) assessed with the International Physical Activity Questionnaire; (ii) a healthy diet (score derived from the Alternate Healthy Eating Index and associated with a lower metabolic risk) evaluated from a food frequency questionnaire; and (iii) exclusive breastfeeding (≥6 months). Women were classified according to the number of preventive practices adopted. Waist circumference, weight, and height were measured and body mass index (BMI) was calculated. Fasting insulinemia and glycemia were obtained and Matsuda index for insulin sensitivity was calculated. Nearly one-third of women adopted none of the listed preventive practices. For each increase of 1 preventive practice adopted, women were 30% less likely to have a BMI ≥ 25 kg·m(-2) (odds ratio (OR): 0.70, 95% confidence interval (CI) (0.50-0.98)), they were 34% less likely to have a waist circumference ≥ 88 cm (OR: 0.66, 95%CI (0.47-0.92)) and they were 33% less likely to have a Matsuda index for insulin sensitivity < 9.69 (OR: 0.67, 95%CI (0.48-0.94)). These results suggest that women with prior GDM who adopt the recommended preventive practices in the years following delivery are less likely to have lower insulin sensitivity, less likely to be overweight-obese, and less likely to be characterized by abdominal obesity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".