Managing Neuroma and Phantom Limb Pain in Ontario: The Status of Targeted Muscle Reinnervation
Bibliographic record
Abstract
Painful neuromas (PN) and phantom limb pain (PLP) are common following amputation and are unreliably treated, which impacts quality of life. Targeted muscle reinnervation (TMR) is a microsurgical technique that repairs the severed proximal nerve end to a redundant motor nerve in the amputated stump. Evidence supports TMR as effective in treating PN and PLP; however, its adoption has been slow. This study aimed to characterize: (1) the populations experiencing post-amputation PN/PLP; (2) current trends in managing PN/PLP; and (3) attitudes toward routine use of TMR to manage PN/PLP. METHODS: A cross-sectional survey was distributed to all orthopedic surgeons, plastic surgeons, and physiatrists practicing in Ontario, via publicly available emails and specialty associations. Data were collected on demographics, experience with amputation, managing post-amputation pain, and attitudes toward routine use of TMR. RESULTS: Sixty-six of 698 eligible participants submitted complete surveys (9.5% response rate). Respondents had a greater experience with surgical management of PN (71% PN versus 10% PLP). However, surgery was considered a 3rd-line option for PN and not an option for PLP in 57% and 59% of respondents, respectively. Thirty participants (45%) were unaware of TMR as an option, and only 8 respondents have currently incorporated TMR into their practice. Many (76%) would be willing to incorporate TMR into their practice as either an immediate or delayed surgical technique. CONCLUSIONS: Despite its promise in managing post-amputation pain, awareness of TMR as a surgical option is generally poor. Several barriers to the widespread adoption of this technique are defined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".