What Makes Transcutaneous Electrical Nerve Stimulation Work? Making Sense of the Mixed Results in the Clinical Literature
Bibliographic record
Abstract
Transcutaneous electrical nerve stimulation (TENS) is a nonpharmacological treatment for control of pain. It has come under much scrutiny lately with the Center for Medicare Services rendering a recent decision stating that "TENS is not reasonable and necessary for the treatment of CLBP [chronic low back pain]." When reading and analyzing the existing literature for which systematic reviews show that TENS is inconclusive or ineffective, it is clear that a number of variables related to TENS application have not been considered. Although many of the trials were designed with the highest of standards, recent evidence suggests that factors related to TENS application need to be considered in an assessment of efficacy. These factors include dosing of TENS, negative interactions with long-term opioid use, the population and outcome assessed, timing of outcome measurement, and comparison groups. The purpose of this perspective is to highlight and interpret recent evidence to help improve the design of clinical trials and the efficacy of TENS in the clinical setting.
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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 | 0.216 | 0.528 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".