Effects of neuromodulation techniques on pain and depression in patients with phantom limb pain: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective This systematic review and meta-analysis aimed to evaluate the efficacy of neuromodulation techniques in alleviating pain and depression in patients with phantom limb pain (PLP). Methods We conducted a comprehensive search of five databases (Medline, Scopus, Embase, Cochrane Library, and Web of Science) up to March 2025, following PRISMA guidelines. Randomized controlled trials (RCTs) investigating central (e.g., rTMS, tDCS) and peripheral (e.g., TENS, NMES, PNS) neuromodulation techniques in PLP patients were included. Primary outcomes were pain reduction, measured by the Visual Analog Scale (VAS) and McGill Pain Questionnaire (MPQ), and depression, assessed using the Beck Depression Inventory (BDI) and Self-Rating Depression Scale (SDS). Data were extracted and analyzed using Review Manager and Stata, with heterogeneity assessed via the I 2 statistic and Q test. Results 17 RCTs involving 510 patients were included. Central neuromodulation techniques, particularly rTMS and tDCS, significantly reduced pain in PLP patients [excitatory M1 rTMS: MD = −1.45, 95%CI (−2.78, −0.11), p = 0.03; anodal M1 tDCS: MD = −1.60, 95%CI (−2.45, 0.74), p = 0.0003]. tDCS with duration >15 min [I 2 = 12%, MD = -1.91, 95%CI (−3.10, 0.72), p = 0.002] and rTMS>7 days treatment [MD = -4.35, 95%CI(−6.34,-2.36), p < 0.0001] were observed significant pooled effects. Peripheral techniques, including TENS and PNS, also showed pain relief, though with fewer studies. No significant improvement in depression. Conclusion Neuromodulation techniques, particularly rTMS and tDCS, are effective in reducing PLP but do not significantly alleviate depression. Further large-scale RCTs with longer follow-ups are needed to confirm these findings and explore the efficacy of other neuromodulation methods. Systematic review registration PROSPERO CRD42022314995.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".