Efficacy of Massage on Pain Intensity in Post-Cesarean Women: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Cesarean section is a common surgical procedure that may be considered a safe alternative to natural birth and helps to resolve numerous obstetric conditions. Still, the Cesarean section is painful; relieving pain after a Cesarean section is crucial, therefore analgesia is necessary for the postoperative period. However, analgesia is not free of complications and contraindications, so massage may be a cost-effective method for decreasing pain post-Cesarean. Our study aims to determine the massage role in pain intensity after Cesarean sections. Methods: We searched five electronic databases for relevant studies. Data were extracted from the included studies after screening procedures. We calculated the pooled mean difference (MD) and standardized mean difference (SMD) for our continuous outcomes, using random or fixed-effect meta-analysis according to heterogenicity status. Interventional studies were assessed for methodological quality using the Cochrane risk-of-bias assessment tool, while observational studies were assessed using the National Institutes of Health’s tools. Results: Our study included 10 RCTs and five observational studies conducted with over 1,595 post-Cesarean women. The pooled MDs for pain intensity considering baseline values either immediately or post 60-90 minutes were favoring the massage group over the control group as follows:(stand. MD = -2.64, 95% CI [-3.80, -1.48], p > .00001; MD = -2.64, 95% CI [-3.80, -1.48], p > .00001, respectively). While pooled MDs regarding post-intervention only either immediately or post 60-90 minutes were:(stand. MD = -2.04, 95% CI [-3.26, -0.82], p = .001; stand. MD = -2.62, 95% CI [-3.52, -1.72], p > .00001, respectively). Conclusion: Our study found that using massage was superior to the control groups in decreasing pain intensity either when the pain was assessed immediately after or 60-90 minutes post-massage application.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".