Neuromuscular electrical stimulation for pain management in combat‐related transtibial amputees during rehabilitation and prosthetic training
Bibliographic record
Abstract
Military members with war‐related lower limb amputation experience a range of acute and chronic pain symptoms. The purpose of this study was to evaluate pain during 12 weeks of a military amputee rehabilitation program (MARP) pre‐ and post‐prosthesis. The data for this study were drawn from a randomized clinical trial comparingMARPsupplemented with neuromuscular electrostimulation (MARP + NMES,n = 23) toMARPalone (n = 21) for service members with unilateral transtibial amputation. The McGill Pain Questionnaire (MPQ) and phantom limb pain/sensations were assessed at baseline, 3, 6, 9, and 12 weeks. Changes within‐ and between‐groups were analyzed with generalized mixed models. Participants reported mild‐to‐moderate pain at all visits, and improved significantly on theMPQand frequency of phantom limb pain/sensations (p < .001 for effect of time). Group by time interactions were not significant, indicating both groups showed similar improvement. Univariate tests showed theNMES + MARPgroup had lower pain intensity thanMARP‐only group at weeks 3 and 6. Participants inMARPdemonstrated good overall pain control and reported reduced pain and fewer days with phantom limb pain/sensations over 12 weeks. AddingNMEStoMARPmay be beneficial in early rehabilitation, andNMEScould potentially enhance physical therapy participation by decreasing pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".