“I don’t know the correct way to describe it”: neuropathic pain experiences among athletes with spinal cord injury
Bibliographic record
Abstract
Background: Chronic pain among athletes is often misinterpreted as tissue damage resulting from sport. While researchers have started to examine neuropathic pain among athletes with spinal cord injury (SCI), there is a need to develop a deeper understanding of their neuropathic pain symptoms and experiences, to support the development of evidence-based pain management protocols. Objectives: The primary purpose of this study was to describe neuropathic pain experienced by athletes with SCI. A secondary purpose was to compare athletes' neuropathic pain symptoms when measured by two different clinical pain assessment tools and describe their experiences when trying to communicate their neuropathic pain. Methods: 47 athletes with SCI completed the International Spinal Cord Injury Pain Basic Data Set (V.2.0), Douleur Neuropathique 4 (DN4), and two open-ended pain questions over the telephone. Results: 4.68±2.92). Overall, participants reported significantly more neuropathic pain symptoms in response to DN4 questions (M=4.62±1.38) versus open-ended pain questions (M=2.13±1.08), p<0.001. Participants reported difficulty with identifying neuropathic pain, describing their pain symptoms and identifying pain locations. Conclusion: Athletes with SCI reported moderate-intensity neuropathic pain. However, they struggled with communicating their neuropathic pain without being prompted with a list of symptoms. To guide the development of effective pain management strategies among athletes with SCI, future research should focus on developing knowledge products to improve awareness of common neuropathic pain descriptors among athletes with SCI and sports medicine personnel.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".