High‐frequency TENS in post‐episiotomy pain relief in primiparous puerpere: A randomized, controlled trial
Bibliographic record
Abstract
AIM: We evaluated the effectiveness of high-frequency transcutaneous electrical nerve stimulation (TENS) as a pain relief resource for primiparous puerpere who had experienced natural childbirth with an episiotomy. METHODS: A controlled, randomized clinical study was conducted in a Brazilian maternity ward. Forty puerpere were randomly divided into two groups: TENS high frequency and a no treatment control group. Post-episiotomy pain was assessed in the resting and sitting positions and during ambulation. An 11-point numeric rating scale was performed in three separate evaluations (at the beginning of the study, after 60 min and after 120 min). The McGill pain questionnaire was employed at the beginning and 60 min later. TENS with 100 Hz frequency and 75 µs pulse for 60 min was employed without causing any pain. Four electrodes ware placed in parallel near the episiotomy site, in the area of the pudendal and genitofemoral nerves. RESULTS: An 11-point numeric rating scale and McGill pain questionnaire showed a significant statistical difference in pain reduction in the TENS group, while the control group showed no alteration in the level of discomfort. Hence, high-frequency TENS treatment significantly reduced pain intensity immediately after its use and 60 min later. CONCLUSION: TENS is a safe and viable non-pharmacological analgesic resource to be employed for pain relief post-episiotomy. The routine use of TENS post-episiotomy is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".