Interpregnancy Weight Gain and Risks of Perinatal Death: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
The link between interpregnancy weight gain (IPWG) and perinatal death remains unclear. This meta-analysis examines the impact of interpregnancy body mass index (BMI) increase on perinatal mortality. A comprehensive search was conducted in July 2024 across MEDLINE, Embase, Cochrane Library, and Web of Science. Observational studies quantitatively reporting IPWG and associated perinatal mortality outcomes were included. IPWG was defined as a weight gain of ≥ 2 BMI units or moving to a higher BMI category between consecutive pregnancies. The risk of bias was assessed using the ROBINS-I tool. Random effects meta-analyses were performed to generate pooled adjusted odds ratios (aORs) and 95% confidence intervals (CIs). The analysis included 10 studies (3,072,698 pregnancies). The primary outcome was perinatal death (deaths occurring at ≥ 20 weeks of gestation up to 28 days postpartum). Secondary outcomes were stillbirth (intrauterine fetal death at ≥ 20 weeks) and neonatal death (deaths among live-born infants within 28 days of life). IPWG significantly increases the risk of perinatal death (aOR, 1.49; 95% CI, 1.32-1.69), stillbirth (aOR, 1.46; 95% CI, 1.23-1.72), and neonatal death (aOR, 1.39; 95% CI, 1.19-1.61), compared to stable interpregnancy weight. These findings suggest that IPWG may increase the risk of perinatal death, highlighting the importance of weight management counseling during the interpregnancy period.
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
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".