Surgical treatments for postamputation pain: study protocol for an international, double-blind, randomised controlled trial
Bibliographic record
Abstract
Abstract Background Painful conditions such as residual limb pain (RLP) and phantom limb pain (PLP) can manifest after amputation. The mechanisms underlying such postamputation pains are diverse and should be addressed accordingly. Different surgical treatment methods have shown potential for alleviating RLP due to neuroma formation — commonly known as neuroma pain — and to a lesser degree PLP. Two reconstructive surgical interventions, namely targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI), are gaining popularity in postamputation pain treatment with promising results. However, these two methods have not been directly compared in a randomised controlled trial (RCT). Here, we present a study protocol for an international, double-blind, RCT to assess the effectiveness of TMR, RPNI, and a non-reconstructive procedure called neuroma transposition (active control) in alleviating RLP, neuroma pain, and PLP. Methods One hundred ten upper and lower limb amputees suffering from RLP will be recruited and assigned randomly to one of the surgical interventions (TMR, RPNI, or neuroma transposition) in an equal allocation ratio. Complete evaluations will be performed during a baseline period prior to the surgical intervention, and follow-ups will be conducted in short term (1, 3, 6, and 12 months post-surgery) and in long term (2 and 4 years post-surgery). After the 12-month follow-up, the study will be unblinded for the evaluator and the participants. If the participant is unsatisfied with the outcome of the treatment at that time, further treatment including one of the other procedures will be discussed in consultation with the clinical investigator at that site. Discussion A double-blind RCT is necessary for the establishment of evidence-based procedures, hence the motivation for this work. In addition, studies on pain are challenging due to the subjectivity of the experience and the lack of objective evaluation methods. Here, we mitigate this problem by including different pain evaluation methods known to have clinical relevance. We plan to analyse the primary variable, mean change in NRS (0–10) between baseline and the 12-month follow-up, using the intention-to-treat (ITT) approach to minimise bias and keep the advantage of randomisation. The secondary outcomes will be analysed on both ITT and per-protocol (PP). An adherence protocol (PP population) analysis will be used for estimating a more realistic effect of treatment. Trial registration ClinicalTrials.gov NCT05009394.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".