The association between weight gain during pregnancy and intertwin delivery weight discordance using 2011–2015 birth registration data from the <scp>USA</scp>
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of weight gain during pregnancy on intertwin delivery weight discordance. METHODS: In the present retrospective cohort study using twin delivery records, data were extracted from the 2011-2015 USA birth registration dataset created by the Centers for Disease Control and Prevention. The outcome variable was delivery weight discordance. The nonlinear association of weight gain during pregnancy with delivery weight discordance was examined using a generalized additive model, adjusting for potential confounders. RESULTS: A total of 255 627 twin pairs were included in this analysis. Weight gain during pregnancy showed an inverse, yet nonlinear, association with intertwin delivery weight discordance. Women with weight gain of approximately 25 kg exhibited the lowest level of discordance. When stratified by pre-pregnancy body mass index, the association of weight gain with discordance became insignificant among obese women. When categorizing weight gain according to recommendations from the Institute of Medicine, inadequate weight gain was associated with increased risk of discordance among women of any pre-pregnancy body mass index. CONCLUSION: Twin pregnancies with maternal weight gain of approximately 25 kg demonstrated the lowest risk of developing intertwin delivery weight discordance, while inadequate weight gain was a risk factor for delivery weight discordance in all pre-pregnancy body mass index categories.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".