SynthesisInternational journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics2026
Efficacy of energy-based devices on episiotomy pain and healing: A systematic review and meta-analysis.
Synthesis in International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpisiotomy is a common obstetric procedure often associated with significant postpartum pain and delayed wound healing. Conventional treatments provide limited relief and might not be suitable for all women. Energy-based therapies, including infrared irradiation and low-level laser therapy (LLLT), a non-thermal photo biomodulation technique, have shown potential for enhancing pain relief and tissue recovery, but their effectiveness in post-episiotomy care remains unclear.
objectiveThis study evaluates the efficacy of energy-based treatments on pain reduction and wound healing following episiotomy in postpartum women.
methodA database search was performed using MEDLINE with Ovid and PubMed interfaces, The Cochrane Central Register of Controlled Trials (CENTRAL), Embase, ClinicalTrials.gov and Web of Science up to December 2024. Prospective randomized and non-randomized controlled trials were considered for inclusion. No restriction was imposed regarding the year or language of publication. Included studies compared one method of energy-based treatment to placebo, standard of care, no treatment or another intervention. Data were synthesized using RevMan Web (Version 8.14.0) with a random-effects model to account for interstudy heterogeneity. Pooled results were expressed as standardized mean differences (SMDs) with 95% confidence intervals. Evidence certainty was assessed using the GRADE approach.
resultsA total of 173 studies were identified through database searches, of which 13 studies were included in the final analysis, encompassing a total of 1377 patients. Analyses were stratified by intervention type: infrared irradiation versus standard care or no treatment (n = 1088), LLLT versus placebo (n = 209) and LLLT versus therapeutic ultrasound (n = 80). Infrared irradiation reduced postpartum pain compared to standard care or no treatment (SMD = -0.50, 95% confidence interval [CI]: -0.98 to -0.02, I
conclusionInfrared therapy might reduce pain after episiotomy, although its effect on wound healing remains inconclusive. LLLT did not demonstrate significant benefits for pain relief or healing. PROSPERO REGISTRATION: CRD42024608543; registered December 2024.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.