Effects from a single application of photobiomodulation on pain intensity from perineal trauma related to childbirth: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To analyze the effects of a single photobiomodulation (PBM) application on perineal pain and healing in women in the immediate postpartum period. METHODS: Randomized controlled trial, double-blind, two-center, with 60 postpartum women with perineal trauma and pain scoring 4 or more on the Numeric Rating Scale (NRS). The experimental group received red light to the lesion and infrared light around it, while the sham group received a simulation. We assessed pain with the NRS and Short-Form McGill Pain Questionnaire (SF-MPQ); tissue healing with the REEDA scale (Redness, Edema, Ecchymosis, Discharge, Approximation) at baseline, 30 min (primary outcome), and 12-36 h after intervention and satisfaction with a Likert scale 7-10 days post-intervention. We conducted bivariate analyses and an analysis of variance for repeated measures. RESULTS: The experimental and sham groups showed pain reduction with no difference between them for pain scores or healing 30 min post-intervention (mean ± SD: NRS 3.63 ± 2.57 vs 2.53 ± 2.15; P = 0.089; SF-MPQ 7.83 ± 8.32 vs 5.10 ± 6.42; P = 0.108; and REEDA 5.57 ± 3.05 vs 4.47 ± 2.42; P = 0.175). Analysis of variance revealed no significant interaction between time and group at any time point evaluated. Of the participants, 84.09% were satisfied or very satisfied with the intervention. CONCLUSIONS: Although both groups experienced a reduction in pain after the intervention, a single PBM application with these parameters was not superior to a sham treatment. Future research could explore multiple applications or different parameters. TRIAL REGISTRATION: Laser for Pain Relief in Nipple and Perineal Trauma in Postpartum; UTN code U1111-1279-3594 (https://ensaiosclinicos.gov.br/rg/RBR-2qm8jrp/1).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".