Passive smoking increased risk of gestational diabetes mellitus independently and synergistically with prepregnancy obesity in Tianjin, China
Bibliographic record
Abstract
BACKGROUND: Passive smoking increased type 2 diabetes mellitus risk, but it is uncertain whether it also increased gestational diabetes mellitus (GDM) risk. We aimed to examine the association of passive smoking during pregnancy and its interaction with maternal obesity for GDM. METHODS: From 2010 to 2012, 12 786 Chinese women underwent a 50-g 1-hour glucose challenge test at 24 to 28 weeks of gestation and further underwent a 75-g 2-hour oral glucose tolerance test if the glucose challenge test result was ≥7.8 mmol/L. GDM was defined by the International Association of Diabetes and Pregnancy Study Group's cut points. Self-reported passive smoking during pregnancy was collected by a questionnaire. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction between maternal obesity and passive smoking was estimated using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S). Significant RERI > 0, AP > 0, or S > 1 indicated additive interaction. RESULTS: A total of 8331 women (65.2%) were exposed to passive smoking during pregnancy. More women exposed to passive smoking developed GDM than nonexposed women (7.8% versus 6.3%, P = 0.002) with an adjusted OR of 1.29 (95%CI, 1.11 to 1.50). Compared with nonobesity and nonpassive smoking, prepregnancy obesity and passive smoking was associated with GDM risk with an adjusted OR of 3.09 (95%CI, 2.38-4.02) with significant additive interaction (P < .05 for RERI and AP). CONCLUSIONS: Passive smoking during pregnancy increased GDM risk in Chinese women independently and synergistically with prepregnancy 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".