The Role of Lactic Acid in Episiotomy Wound Healing: A Systematic Review
Bibliographic record
Abstract
Episiotomy is a common obstetric intervention aimed at facilitating childbirth and reducing severe perineal trauma. Lactic acid, a naturally occurring alpha-hydroxy acid (AHA), has emerged as a promising alternative to conventional wound-care methods due to its antimicrobial, anti-inflammatory, and regenerative properties. Objective: This systematic review evaluates the effectiveness of lactic acid in episiotomy wound healing compared to conventional wound-care methods, focusing on healing time, infection rates, and patient-reported outcomes. Methods: A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, and Scopus using the keywords “lactic acid”, “episiotomy wound healing”, “perineal wound care”, and “infection prevention”. Inclusion criteria covered randomized controlled trials (RCTs), observational studies, and systematic reviews. The Cochrane Risk of Bias 2 (RoB 2) tool and the Newcastle–Ottawa Scale were used for quality assessment. Results: Eight studies met the inclusion criteria. Lactic acid-treated wounds demonstrated 30% faster healing rates, 50% lower infection rates, and reduced pain scores compared with standard wound-care methods (e.g., povidone-iodine or saline). A meta-analysis of five RCTs found a significant reduction in post-episiotomy infections (RR = 0.68, 95% CI: 0.52–0.85). Conclusions: Lactic acid shows promise in episiotomy wound care by improving healing outcomes and reducing infection and discomfort. However, further large-scale RCTs are needed to confirm its safety and long-term efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".