Association of Shoulder Pain With the Use of Mobility Devices in Persons With Chronic Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of shoulder pain and its association with the use of assistive devices for mobility in persons with chronic spinal cord injury (SCI). DESIGN: Cross-sectional analysis conducted within a cohort study. SETTING: SCI service in a hospital and the community. PARTICIPANTS: Between August 2005 and January 2008, 93 participants with chronic SCI completed a standardized health questionnaire and pain questionnaire. MAIN OUTCOME MEASURES: Shoulder pain in last 6 months elicited by use of the McGill Pain Questionnaire pain diagram. RESULTS: Of the 93 participants, 65 (69.9%) reported pain at any site in the 6 months before testing. Shoulder pain, reported by 39.8% of participants, was the third most common site of pain after the legs and back. When stratified by the use of assistive mobility devices, shoulder pain was reported by 46.7% of motorized wheelchair users, 35.4% of manual wheelchair users, 47.6% of participants using aids such as crutch(es) or canes, and 33.3% of participants walking without assistance (P = .7 for comparison of 4 groups). CONCLUSIONS: Shoulder pain is highly prevalent in SCI. The authors of previous studies have largely attributed shoulder pain in SCI to manual wheelchair use. However, our results provide evidence for similarly elevated prevalence of shoulder pain among motorized wheelchair users and those patients using crutches or canes. This finding suggests that in addition to overuse injury from cyclic wheelchair propulsion, the assessment of other mechanical and nonmechanical factors that lead to shoulder pain in SCI is an unmet research need that may have treatment implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".