Additive Analgesic Effect of Transcranial Direct Current Stimulation Together with Mirror Therapy for the Treatment of Phantom Pain
Bibliographic record
Abstract
OBJECTIVE: Current analgesic treatments for phantom pain are not optimal. One well-accepted yet limited nonpharmacological option is mirror therapy, which is thought to counterbalance abnormal plasticity. Transcranial direct current stimulation (tDCS) is an emerging approach believed to affect the membrane potential and activity threshold of cortical neurons. tDCS analgesic effectiveness, however, is mild and short, rendering it a noneffective stand-alone treatment. This study aimed to assess if a combination of mirror therapy with tDCS results in a superior analgesic effect as compared with mirror therapy alone in patients suffering from phantom pain due to recent amputation. DESIGN: Following ethical approval, eligible patients provided informed consent and were randomly assigned to a study treatment group that continued for 2 weeks (once daily): 1) mirror therapy; 2) mirror therapy and sham tDCS; or 3) mirror therapy and tDCS. Assessments were done before treatment; at the end of treatment weeks 1 and 2; and at 1 week, 1 month, and 3 months following treatment. The primary outcome measure was pain intensity. Secondary measures were derived from the Short Form McGill Pain Questionnaire and the Brief Pain Inventory. RESULTS: Thirty patients were recruited, and 29 patients completed the study. Three months following treatment, pain intensity was significantly (P<0.001) reduced in the combined treatment group (reduction of 5.4±3.3 points) compared with the other study arms (mirror therapy, 1.2±1.1; mirror therapy and sham tDCS, 2.7±3.2). All secondary outcome results were in line with these findings. CONCLUSIONS: Combining tDCS with mirror therapy results in a robust long-lasting analgesic effect. These encouraging findings may contribute to the understanding of the underlying mechanisms of phantom pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".