Short Body Height and Pre-pregnancy Overweight for Increased Risk of Gestational Diabetes Mellitus: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Short height is associated with gestational diabetes mellitus (GDM) but the underlying mechanism remains unknown. This study aimed to explore whether short height has a synergistic effect with pre-pregnancy overweight/obesity and undue weight gain on the risk of GDM. Methods: We recruited 19,962 singleton pregnant women from their first antenatal care visit in urban Tianjin, China, between October 2010 to August 2012. At 24-28 weeks of gestation, women underwent a 50-gram 1-hour glucose challenge test followed by a 75-gram 2-hour oral glucose tolerance test if the GCT result was ≥7.8 mmol/L. GDM was defined by the International Association of Diabetes and Pregnancy Study Group’s cut-points. Univariable and multivariable logistic regression analyses were performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic spline analysis nested in the logistic regression analysis was used to identify a cutoff point of height for GDM. Additive interaction was used to test interactions between short height, pregnancy overweight and undue weight gain. Results: A total of 1,517 (or 7.6%) women developed GDM. The risk of GDM increased rapidly with a decreasing height from 158 cm and downwards. Using height ≥158 cm as the reference group, women with 0.05). Conclusions: In Chinese pregnant women in urban Tianjin, height <158 cm had a synergistic effect with pre-pregnancy overweight on the risk of GDM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".