Transcutaneous electrical nerve stimulation and conditioned pain modulation influence the perception of pain in humans
Bibliographic record
Abstract
BACKGROUND: Research in animal models suggests that transcutaneous electrical nerve stimulation (TENS) and conditioned pain modulation (CPM) produce analgesia via two different supraspinal pathways. No known studies have examined whether TENS and CPM applied simultaneously in human subjects will enhance the analgesic effect of either treatment alone. The purpose of the current study was to investigate whether the simultaneous application of TENS and CPM will enhance the analgesic effect of that produced by either treatment alone. METHODS: Sixty healthy adults were randomly allocated into two groups: (1) CPM plus active TENS; (2) CPM plus placebo TENS. Pain threshold for heat (HPT) and pressure (PPT) were recorded from subject's left forearm at baseline, during CPM, during active or placebo TENS, and during CPM plus active or placebo TENS. CPM was induced by placing the subjects' contralateral arm in a hot water bath (46.5 °C) for 2 min. TENS (100 μs, 100 Hz) was applied to the forearm for 20 min at a strong but comfortable intensity. RESULTS: Active TENS alone increased PPT (but not HPT) more than placebo TENS alone (p = 0.011). Combining CPM and active TENS did not significantly increase PPT (p = 0.232) or HPT (p = 0.423) beyond CPM plus placebo TENS. There was a significant positive association between PPT during CPM and during active TENS (r(2) = 0.46; p = 0.003). CONCLUSIONS: TENS application increases PPT; however, combining CPM and TENS does not increase the CPM's hypoalgesic response. CPM effect on PPT is associated with the effects of TENS on PPT.
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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) | 0.000 | 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) | 0.004 | 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".