Excess Weight Gain in Pregnant Women and Prematurity: A Meta-Analysis
Bibliographic record
Abstract
Background: Premature birth as a cause of morbidity and mortality in neonates. Excessive weight gain in pregnant women is considered a risk factor for adverse pregnancy outcomes including preterm birth. This study aims to analyze the effect of excess weight gain in pregnant women on premature birth. Subjects and Method: This research is a systematic review and meta-analysis. Article searches were conducted using electronic databases such as Google Scholar, PubMed, Science Direct and Springerlink. The articles used are articles published from 2011-2021. The keywords to search for articles were: “gestational weight gain” AND “pregnancy” AND (“preterm birth” OR “premature birth”) AND “cohort study” AND “adjusted odds ratio”. The inclusion criteria used were full text articles in English with a cohort study design, multivariate analysis with Adjusted Odds Ratios (aOR), research subjects were pregnant women, intervention was excessive weight gain, comparison was normal weight gain (adequate). , the study outcome was preterm delivery (<37 weeks). The article search results are listed in the PRISMA diagram and analyzed using the Review Manager 5.3 application. Results: A total of 10 cohort study articles from China, Indonesia, Canada, Korea, Mexico, Puerto Rico, Saudi Arabia, and Taiwan were selected for systematic review and meta-analysis. The results showed that excess weight gain in pregnant women increased the risk of preterm birth and was statistically significant (aOR= 1.23; 95% CI= 1.01 to 1.48; p= 0.030). Conclusion: Excess weight gain in pregnant women increases the risk of premature birth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".