A scoping review of current non-pharmacological treatment modalities for phantom limb pain in limb amputees
Bibliographic record
Abstract
PURPOSE: Phantom limb pain (PLP) is a chronic neuropathic pain condition of a missing limb following amputation. Pain management is multi-modal, including various non-pharmacological therapies. The purpose of this scoping review was to investigate the evidence surrounding current non-pharmacological treatment modalities for PLP and provide insight into their clinical feasibility. METHOD: A systematic search was conducted using four databases (Medline, Embase, PsychInfo, and CINAHL) following the PRISMA-ScR method. Results from papers meeting the inclusion criteria were charted to summarize findings, demographics, and use of neuroimaging. RESULTS: A total of 3387 papers were identified, and full texts of 142 eligible papers were assessed. Eleven treatment modalities for PLP were identified with varying levels of evidence. Overall, there were 25 RCTs, 58 case reports, and 59 a combination of pilot, quasi-experimental, observational, and other study designs. CONCLUSIONS: Currently, the evidence surrounding most treatment modalities is limited and only a fraction of studies are supported by strong evidence. The findings of this review demonstrated a clear need to conduct more rigorous research with diverse study designs to better understand which modalities provide the most benefit and to incorporate neuroimaging to better determine the neural correlates of PLP and mechanisms of various treatments.Implications for RehabilitationPhantom limb pain (PLP) is a prevalent and debilitating condition following amputation and health care professionals should incorporate an evidence-based pain management protocol into their rehabilitation program.There exist a number of different non-pharmacological therapies to address PLP, however the scientific rigor and levels of evidence vary across modalities.Prescription of interventions for PLP should consider individual patient differences, accessibility to the patient, and quite possibly, a multi-modal approach, particularly for those who also experience residual limb pain.Imagery-based therapies provide the highest level of current evidence based on robust and large randomized control trials, are readily accessible, and are thus most recommended for relief of PLP.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".