Botulinum toxin therapy in amputee pain management
Bibliographic record
Abstract
Background: Post-amputation pain is common, occurring in up to 85% of patients. The pain can be related to the etiology of amputation, post-surgical healing, or prosthetic use. Pain syndromes may arise from a variety of tissue pathologies and can be broadly categorized into residual limb pain (RLP) or phantom limb pain (PLP). Botulinum toxin (BTX) has been found to be effective in treating a variety of neuropathic pain conditions. This scoping review summarizes the use of BTX in RLP and PLP management of patients with a major extremity amputation. Methods: A literature search was conducted using PubMed, Web of Science, Cochrane Library, Scopus, and Google Scholar. Sixteen studies were included. Most studies excluded did not address BTX use in amputee pain management. Extracted data were categorized by either RLP or PLP. Results: Two randomized controlled trials (RCTs), 10 case series, and 4 case reports were included (total 68 patients, 82 amputations). Seven studies addressed BTX use in both RLP and PLP, 5 studies address RLP exclusively, and 5 additional studies exclusively addressed PLP. Toxin types, injection techniques, and dosages varied between the studies. Negative results were reported in 2 RCTs and 2 case series showing 30% of patients with RLP and 50% patients with PLP did not benefit from BTX. Conclusion: Literature for BTX in PLP and RLP is broad but lacking rigour for definitive conclusions to guide usage. There were more positive results for BTX use in RLP than in PLP. Case reports and patient series show promising results for both PLP and RLP, indicating future research should be directed at adequately-powered prospective trials.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".