Interactive effects of gestational diabetes and high pre‐pregnancy body mass index on adverse growth patterns of offspring
Bibliographic record
Abstract
AIMS: To examine the independent and interactive effects of maternal gestational diabetes mellitus (GDM) and high pre-pregnancy body mass index (BMI) on the risk of offspring adverse growth patterns. MATERIALS AND METHODS: One thousand six hundred and eighty one mother-child pairs were followed for 8 years in Tianjin, China. Group-based trajectory modelling was used to identify offspring growth patterns. Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs) of GDM and high pre-pregnancy BMI for offspring adverse growth patterns. Restricted cubic spline was used to identify cut-off points. Additive interactions and multiplicative interactions were used to test interactive effects between GDM and high pre-pregnancy BMI for adverse growth patterns. RESULTS: Four distinct growth patterns were identified in offspring, including normal growth pattern, persistent lean growth pattern, late obesity growth pattern (LOGP), and persistent obesity growth pattern (POGP). Maternal high pre-pregnancy BMI was associated with LOGP and POGP (adjusted OR, 95% CI: 2.38, 1.74-3.25 & 4.92, 2.26-10.73). GDM greatly enhanced the adjusted OR of high pre-pregnancy BMI for LOGP up to 3.48 (95% CI: 2.25-5.38). Additive interactions and multiplicative interactions between both risk factors were significant for LOGP but not for POGP. CONCLUSIONS: Maternal high pre-pregnancy BMI was associated with increased risk of LOGP and POGP, whereas GDM greatly enhanced the risk of high pre-pregnancy BMI for LOGP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".