Are women who quit smoking at high risk of excess weight gain throughout pregnancy?
Bibliographic record
Abstract
BACKGROUND: Smoking cessation has been reported to be associated with high total gestational weight gain (GWG), which itself is a risk factor for adverse maternal-infant outcomes. Recent studies have criticized conventional single measures of GWG, since they may lead to biased results. Therefore, we aimed to compare patterns of GWG based on serial antenatal weight measurements between women who: never smoked, quit during pregnancy, continued to smoke. METHODS: Participants (N = 509) of our longitudinal study were recruited from seven antenatal clinics in Southwestern Ontario. Serial GWG measurements were abstracted from medical charts, while information on smoking status was obtained from a self-administered questionnaire at a median gestational age of 32 (27-37) weeks. GWG patterns were assessed by fitting piecewise mixed-effects models. First trimester weight gains and weekly rates for the last two trimesters were compared by smoking status. RESULTS: During the first trimester, women who never smoked and those who quit during pregnancy gained on average 1.7 kg (95 % CI: 1.4-2.1) and 1.2 kg (0.3-2.1), respectively, whereas women who continued smoking gained more than twice as much (3.5 kg, 2.4-4.6). Weekly rate of gain in the second and third trimesters was highest in women who quit smoking (0.60 kg/week, 0.54-0.65), approximately 20 and 50 % higher than in women who never smoked and those who smoked during pregnancy, respectively. CONCLUSIONS: In this longitudinal study to examine GWG by smoking status based on serial GWG measurements, we found that women who quit smoking experienced a rapid rate of gain during the last two trimesters, suggesting that this high-risk group may benefit from targeted interventions.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".