Effect of dry cupping therapy and acupuncture on the pain scale in low back pain in postpartum women: A systematic review and meta-analysis
Bibliographic record
Abstract
Context: Dry cupping and acupuncture are complementary treatments that can be used to decrease low back pain (LBP) in postpartum mothers. Aims: To analyze the pain intensity after each treatment measured by visual analog scale (VAS) in postpartum mothers with LBP. Methods: Articles for this systematic review and meta-analysis were retrieved from Web of Science, Science Direct, PubMed, Google Scholar, and Scopus (10/1/2013-12/31/2022), adjusted for inclusion and exclusion criteria, and filtered according to PRISMA guidelines, and the meta-analysis was using Review Manager 5.4. The Newcastle-Ottawa scale was selected to ascertain the quality of each included paper. Results: Four randomized controlled- trials (RCTs) with 157 patients were included. VAS scores from these studies showed that after acupuncture and dry cupping treatment, the VAS scores were significantly lower in LBP-postpartum mothers than in controls (SMD IV, Random 95%: 1.69 [95% I2 = 83%; p = 0.00001]) and (SMD IV, Random, 95%: 2.06 [95% I2 = 63 %; p = 0.0001]), respectively. The LBP among these postpartum mothers can be triggered by perinatal stress, postpartum baby blues, and other back pain-related morbidities. Conclusions: Both acupuncture and dry cupping effectively reduced LBP in postpartum mothers. However, dry cupping showed a higher impact and was arguably more beneficial due to this method's non-invasiveness. Furthermore, acupuncture and dry cupping are considered safe treatments after giving birth because they do not interfere with breast milk production.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".