Tailored transcutaneous electrical nerve stimulation improves dysesthesia in individuals with spinal cord injury: A randomized N-of-1 trial
Bibliographic record
Abstract
Context Current therapeutic interventions are often ineffective for dysesthesia, including tingling and allodynia, caused by spinal cord injury (SCI). Dysesthesia-matched TENS (DM-TENS) is an innovative approach for dysesthesias that customizes stimulation parameters to align with an individual’s specific dysesthesia characteristics.Objective We aimed to evaluate the efficacy of DM-TENS for dysesthesia in individuals with SCI.Design A randomized, placebo-controlled, aggregated N-of-1 trial was conducted in six individuals with SCI.Methods Participants received either DM-TENS as an intervention or sham TENS as a control. Each treatment was administered for 60 min/day over a 7-day period. The outcome measures included the Numeric Rating Scale (NRS) for dysesthesia and Short-Form McGill Pain Questionnaire-2 (SF-MPQ2).Results Using a hierarchical Bayesian model, we found that dysesthesia-matched TENS provided clinically meaningful improvements in dysesthesia among individuals with SCI, with a high posterior probability (96–100%) exceeding the minimal clinically meaningful difference (NRS of 2.70) at both the individual and population levels. The mean NRS was 5.79 (95% credible interval [95% CI]: 4.58-7.01) during the placebo period, and 1.12 (95% CI: 0.59-1.66) during the DM-TENS period. In the SF-MPQ-2, the dysesthesia-matched TENS effects showed decisive evidence (BF10 > 1000) for items on tender, pain caused by light touch, tingling or pins and needles, and numbness.Conclusions This series of N-of-1 trials comparing dysesthesia-matched TENS with placebo indicated a reduction in dysesthesia in individuals with SCI. Thus, dysesthesia-matched TENS is a promising therapeutic option for managing dysesthesia in this population.Trial registration: UMIN Japan identifier: UMIN000050250..
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".