High- and low-frequency transcutaneous electrical nerve stimulation does not reduce experimental pain in elderly individuals
Bibliographic record
Abstract
Despite its widespread clinical use, the efficacy of transcutaneous electrical nerve stimulation (TENS) remains poorly documented in elderly individuals. In this randomized, double-blind crossover study, we compared the efficacy of high-frequency (HF), low-frequency (LF), and placebo (P) TENS in a group of 15 elderly adults (mean age: 67 ± 5 years). The effect of HF-, LF-, and P-TENS was also evaluated in a group of 15 young individuals (26 ± 5 years; same study design) to validate the effectiveness of the TENS protocols that were used in the elderly group. Each participant came to the laboratory on 3 separate occasions to receive, in random order, HF-, LF-, and P-TENS. Pain intensity and pain perception thresholds were assessed before, during, and after TENS, using an experimental heat pain paradigm. For the young group, there was a significant decrease in pain intensity during and after HF- and LF-TENS when compared with baseline, with both HF- and LF-TENS being superior to P-TENS. In the older group, HF- and LF-TENS did not reduce pain when compared with baseline and no difference was observed between the 2 active TENS sessions and P-TENS. High-frequency, LF-, and P-TENS all increased pain thresholds in young individuals, whereas in older individuals, only LF-TENS increased pain thresholds. Taken together, these results suggest that TENS is effective in young, but not in older, individuals. Future studies should be conducted to confirm these results in pain populations and to identify strategies that could enhance the effect of TENS in the elderly.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".