Comparison of photobiomodulation with cryotherapy in the immediate postpartum period of parturients with grade I, grade II lacerations and/or episiotomy in reducing perineal and vulvar and edema: A randomized clinical trial
Bibliographic record
Abstract
INTRODUCTION: Vaginal delivery has several benefits for the parturient; however, during labor, some injuries, such as lacerations and/or episiotomy, can occur. Perineal pain may occur in the puerperium and can be aggravated in cases of perineal injury during childbirth, potentially impacting the physical and emotional aspects of the parturient. For this reason, it is necessary to use techniques that can relieve pain and edema in the immediate postpartum period, directly influencing recovery. OBJECTIVE: To compare the reduction of pain and improvement in healing using two techniques, namely photobiomodulation and cryotherapy, performed in the immediate postpartum period of up to 12 h, in parturients who suffered grade I and II lacerations and/or episiotomy. METHODS: Data collection was carried out through an evaluation questionnaire. Photobiomodulation was applied using the red and infrared laser from the DMC brand. The EVA and McGill scales were used for pain assessment, and the REEDA scale was used for the evaluation of edema and healing. RESULTS: The techniques were evaluated and applied to 56 patients, with 28 in each group (cryotherapy and LBI). Patients who received photobiomodulation showed superior improvement compared to cryotherapy. In the immediate postpartum period, there was a greater reduction in pain in favor of photobiomodulation (p = 0.008); and after 24 h, the difference was even more significant (p < 0.001).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".