Efficacy of Transcutaneous Electrical Nerve Stimulation (TENS) for Chronic Low-back Pain in a Multiple Sclerosis Population
Bibliographic record
Abstract
OBJECTIVE: This study was designed to investigate the hypoalgesic effects of self-applied transcutaneous electrical nerve stimulation (TENS) on chronic low-back pain (LBP) in a multiple sclerosis (MS) population. METHODS: Ninety participants with probable or definite MS (aged 21 to 78 y) presenting with chronic LBP were recruited and randomized into 3 groups (n=30 per group): (1) low-frequency TENS group (4 Hz, 200 micros); (2) high-frequency TENS group (110 Hz, 200 micros); and (3) placebo TENS. Participants self-applied TENS for 45 minutes, a minimum of twice daily, for 6 weeks. Outcome measures were recorded at weeks 1, 6, 10, and 32. Primary outcome measures included: Visual Analog Scale for average LBP and the McGill Pain Questionnaire. Secondary outcome measures included: Visual Analog Scale for worst and weekly LBP, back and leg spasm; Roland Morris Disability Questionnaire; Barthel Index; Rivermead Mobility Index; Multiple Sclerosis Quality of Life-54 Instrument, and a daily logbook. Data were analyzed blind using parametric and nonparametric tests, as appropriate. RESULTS: Results indicated a statistically significant interactive effect between groups for average LBP (P=0.008); 1-way analysis of covariance did not show any significant effects at any time point once a Bonferonni correction was applied (P>0.05). However, clinically important differences were observed in some of the outcome measures in both active treatment groups during the treatment and follow-up periods. DISCUSSION: Although not statistically significant, the observed effects may have implications for the clinical prescription and the use of TENS within this population.
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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) | 0.001 | 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".