Effects of Combined and Alone Transcranial Motor Cortex Stimulation and Mirror Therapy in Phantom Limb Pain: A Randomized Factorial Trial
Bibliographic record
Abstract
Phantom limb pain (PLP) is a frequent complication in amputees, which is often refractory to treatments. We aim to assess in a factorial trial the effects of transcranial direct current stimulation (tDCS) and mirror therapy (MT) in patients with traumatic lower limb amputation; and whether the motor cortex plasticity changes drive these results. In this large randomized, blinded, 2-site, sham-controlled, 2 × 2 factorial trial, 112 participants with traumatic lower limb amputation were randomized into treatment groups. The interventions were active or covered MT for 4 weeks (20 sessions, 15 minutes each) combined with 2 weeks of either active or sham tDCS (10 sessions, 20 minutes each) applied to the contralateral primary motor cortex. The primary outcome was PLP changes on the visual analogue scale at the end of interventions (4 weeks). Motor cortex excitability and cortical mapping were assessed by transcranial magnetic stimulation (TMS). We found no interaction between tDCS and MT groups ( F = 1.90, P = .13). In the adjusted models, there was a main effect of active tDCS compared to sham tDCS (beta coefficient = −0.99, P = .04) on phantom pain. The overall effect size was 1.19 (95% confidence interval: 0.90, 1.47). No changes in depression and anxiety were found. TDCS intervention was associated with increased intracortical inhibition (coefficient = 0.96, P = .02) and facilitation (coefficient = 2.03, P = .03) as well as a posterolateral shift of the center of gravity in the affected hemisphere. MT induced no motor cortex plasticity changes assessed by TMS. These findings indicate that transcranial motor cortex stimulation might be an affordable and beneficial PLP treatment modality.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".