Association of oxytocin augmentation with postpartum hemorrhage: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective The current study aims to evaluate the correlation between oxytocin augmentation and postpartum hemorrhage.Method PubMed, Web of Science, and Scopus has been searched for studies assessing the correlation between oxytocin augmentation and postpartum hemorrhage up to January 24, 2024. The search strategy included relevant keywords related to PPH and oxytocin augmentation. The risk of bias assessment was conducted by two reviewers using the Newcastle-Ottawa Scale (NOS). To pool the effects sized of included studies odds ratios (OR) of interest outcome with their 95% confidence interval (CI) were used.Results Eight studies were included in this meta-analysis. The pooled analysis of the included studies showed a statistically significant association between oxytocin augmentation and increased odds of PPH (pooled odds ratio [OR] = 1.27, 95% confidence interval [CI]: 1.05-1.53; I2 = 84.94%; p = 0.01). Publication bias was assessed using funnel plots, which appeared relatively asymmetrical, indicating significant publication bias. Galbraith plot and trim and fill plot were used for publication bias. Sensitivity analyses were performed by leave one out method.Conclusion This meta-analysis suggests that using oxytocin for labor augmentation is linked to a significant increase in the risk of PPH. It highlights the need for careful monitoring and consideration when using oxytocin, especially in low and middle-income countries where guidelines and supervision are crucial.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".