Association of Maternal Weight and Gestational Weight Gain with Maternal and Neonate Outcomes: A Prospective Cohort Study
Bibliographic record
Abstract
We investigated the association of outcomes with pre-pregnancy body mass index (ppBMI), Institute of Medicine (IOM) recommendations about gestational weight gain, and weight gain trajectories during pregnancy. A prospective cohort of 7866 pregnant women was recruited. ppBMI and weight gain at each follow up visit were collected. The outcomes were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), caesarean delivery, macrosomia, small (SGA) and large (LGA) for gestational age, neonatal hypoglycemia. Group-based multi-trajectory modelling was used for weight kinetics during pregnancy. In the third trimester, 53.8% of women were above IOM recommendations, with an increased relative risk (RR) of HDP (1.91 (1.40–2.61)), caesarean (1.34 (1.15–1.56)), macrosomia (2.17 (1.77–2.67)), LGA (2.26 (1.83–2.80)), and hypoglycemia (1.89 (1.12–3.18)). Women with a weight gain above IOM recommendations in the second trimester who normalized their weight gain in third trimester had, compared to those who remained above IOM recommendations, fewer events of HDP (2.8% versus 5.3%, p = 0.008), caesarean delivery (16.9% versus 22%, p = 0.006), macrosomia (8.3% versus 14.2%, p < 0.001), and LGA (7% versus 13.2%, p < 0.001). Multi-trajectory modelling identified three profiles with continued variation in RR of complications, including GDM. Weight gain above IOM recommendations increased the risk of perinatal complications. A correction of excessive weight gain in the second trimester reduces these risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".