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.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".