Adjusting Pulse Amplitude During Transcutaneous Electrical Nerve Stimulation Does Not Provide Greater Hypoalgesia
Bibliographic record
Abstract
OBJECTIVES: Transcutaneous electrical nerve stimulation (TENS) is an electrotherapeutic modality commonly used in rehabilitation to relieve pain. Adjusting pulse amplitude (intensity) during TENS treatment has been suggested to overcome nerve habituation. However, it is still unclear if this procedure leads to greater hypoalgesia. The aim of this study was to determine if the hypoalgesic effect of TENS is greater when pulse amplitude is adjusted throughout the TENS treatment session in chronic low-back pain patients. DESIGN: Randomized double-blind crossover study. SETTING: Recruitment and assessment were conducted at the Clinique universitaire de réadaptation de l'Estrie (CURE) of the Faculty of Medicine and Health Sciences of the Université de Sherbrooke. PARTICIPANTS: Twenty-one volunteers with chronic low-back pain were enrolled and completed this investigation. INTERVENTIONS: Each patient received two high-frequency TENS treatments on two separate sessions: (1) with adjustment of pulse amplitude and (2) without pulse amplitude adjustment. MAIN OUTCOME MEASURES: Pain intensity and unpleasantness were assessed before, during, and after TENS application with a 10 cm visual analog scale. RESULTS: Both TENS conditions (with and without adjustment of intensity) decreased pain intensity and unpleasantness when compared with baseline. No difference was observed between the two stimulation conditions for both pain intensity and unpleasantness. CONCLUSION: The current results suggest that adjustment of pulse amplitude during TENS application does not provide greater hypoalgesia in individuals with chronic low-back pain. Future studies are needed to confirm these findings in other pain populations.
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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 | 0.002 |
| 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) | 0.003 | 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".