High and Low Frequency TENS Reduce Postoperative Pain Intensity After Laparoscopic Tubal Ligation
Bibliographic record
Abstract
BACKGROUND: Transcutaneous electrical nerve stimulation (TENS) is an effective adjunctive therapy for postoperative pain; however, effects of different frequencies of stimulation have not been systematically investigated. Laparoscopic sterilization (LS) causes significant pain in the early postoperative period and requires substantial postoperative medication. Therefore, we studied the effects of TENS on postoperative pain after LS through placement of Yoon fallopian rings in a prospective, randomized, double-blinded, and placebo-controlled study. METHODS: Sixty-four patients undergoing LS for uterine tube ligation were randomly allocated to receive either active TENS or placebo TENS. Postoperative pain was evaluated using a standard 11-point numeric rating scale and the McGill Pain Questionnaire (MPQ)-pain rating index and number of words chosen. Both high frequency (100 Hz) and low frequency (4 Hz) TENS, at strong, but comfortable sensory intensity, were applied for 20 minutes through 4 electrodes placed around the surgical incision immediately after surgery. Pain was assessed before and after application of TENS when patients were at postanesthesia care unit (PACU). RESULTS: Both high and low frequency TENS significantly decreased postoperative pain intensity when compared with before administration of TENS using the numeric rating scale (P=0.001), pain rating index (P=0.001), and number of words chosen (P=0.001) compared with placebo TENS (P=0.001). TENS in combination with standard pharmacologic analgesic treatment was efficacious for postoperative pain relief after LS. CONCLUSIONS: We recommend regular use of multimodal therapy with TENS and analgesic drugs after LS with placement of Yoon rings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".