Postpartum pain and the risk of postpartum depression: A meta‐analysis of observational studies
Bibliographic record
Abstract
Abstract Objective: This meta‐analysis of observational studies aimed to derive a more precise estimation of the relationship between postpartum pain and postpartum depression (PPD). Methods: A systematic literature search was completed in the following databases from inception to September 26, 2022: PubMed, Embase, and Web of Science. Quality evaluation of each study was achieved through Newcastle‐Ottawa scale (NOS) assessment. Heterogeneity across studies was evaluated by Cochran's Q test and I 2 test. Pooled estimates of odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were analyzed using fixed‐effects model or random‐effects model, according to heterogeneity. Subgroup analysis, sensitivity analysis, and Egger's test were also performed. Results: From the identified 1884 articles, a total of 8 studies involving 3973 participants were included in the final meta‐analysis. Seven of the 8 studies were evaluated as high‐quality, with NOS scores ≥7. A significant heterogeneity was observed ( I 2 = 66.5%, p = 0.004) among eight studies. Therefore, the performed random‐effect model suggested a significant association between postpartum pain and PPD risk (OR 1.29, 95% CI 1.10–1.52, p = 0.002). However, the subgroup analyses did not define the source of heterogeneity. Moreover, the sensitivity analysis showed the stability of the pooled results, but the significant publication bias was identified ( p = 0.009). The trim and fill method was performed and resulted in an OR of 1.14 (95% CI 0.95–1.37, p = 0.162). Conclusions: This meta‐analysis found a potential association between postpartum pain and PPD. Further researches are needed to provide more robust evidences.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".