Duration of Analgesia Induced by Acupuncture-Like TENS on Experimental Heat Pain
Bibliographic record
Abstract
Background. Acupuncture-like TENS (AL-TENS) is a treatment modality that can be used to temporarily reduce pain. However, there is no clear data in the literature regarding the specific duration of analgesia induced by AL-TENS. Objectives. To describe and quantify the duration and magnitude of AL-TENS analgesia on experimental heat pain in healthy subjects and verify if the duration or magnitude of analgesia induced by the AL-TENS was influenced by the duration of the application of the AL-TENS (15 versus 30 minutes). Methods. A repeated-measures, intrasubject randomized experimental design was used, where each participant was his/her own control. 22 healthy volunteers underwent heat pain stimulations with a contact thermode before (pretest) and after (posttest) AL-TENS application (15 and 30 minutes). Outcome measures included subjective pain during AL-TENS, duration, and magnitude of AL-TENS-induced analgesia. Results. Survival analysis showed that the median duration of AL-TENS analgesia was 10 minutes following the application of either 15 or 30 minutes of AL-TENS. The magnitude of analgesia following either application was comparable at all points in time (P values > 0.05) and ranged between -20% and -36% pain reduction. Conclusion. Only half of the participants still had heat-pain analgesia induced by the AL-TENS at 15 minutes postapplication.
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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) | 0.000 | 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) | 0.002 | 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".