A Comparison of Neuropathic Pain Experiences Among Paralympic Versus Recreational Athletes with Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Individuals with spinal cord injury (SCI) report high levels of neuropathic pain. Current treatment options are primarily pharmaceutical, despite their limited effectiveness. Exercise may reduce neuropathic pain among persons with SCI; however, the optimal dose of exercise required to elicit analgesic effects remains unknown. The purpose of this study was to compare neuropathic pain intensity, pain catastrophizing, use of coping strategies, and positive affect and well-being among Paralympic versus recreational athletes with SCI who experience chronic neuropathic pain. Forty-seven athletes with SCI (25 Paralympic, 27 recreational) completed the International SCI Pain Basic Data Set, Douleur Neuropathique-4, coping strategies questionnaire, pain catastrophizing scale, and SCI-quality of life assessment. RESULTS: Paralympic athletes reported significantly greater neuropathic pain (p = 0.032) and positive affect and well-being (p = 0.047) than recreational athletes. No other comparisons were significant (ps > 0.09). Significant, medium-sized positive correlations were observed between neuropathic pain and total minutes of moderate-intensity exercise (r = 0.335, p = 0.023) and average minutes per day of moderate-intensity exercise (r = 0.375, p = 0.010) over the past week. CONCLUSIONS: The results suggest that frequent moderate- to high-intensity exercise may exacerbate neuropathic pain sensations for persons with SCI. Research should investigate psychosocial and physiological mechanisms by which exercise may influence neuropathic pain to explain how Paralympic athletes with SCI are able to continue exercising while maintaining positive affect despite neuropathic 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".