Long-term musculoskeletal complaints after traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To examine the extent and impact of musculoskeletal complaints in a TBI survivor population many years post-injury. METHODS: A retrospective cohort design was used. Former patients of a rehabilitation programme who sustained a moderate-to-severe TBI 15 or more years prior (n = 34) were interviewed by telephone. The mean age at follow-up was 48 years; mean time post-injury was 26 years. MAIN OUTCOMES: Seventy-nine per cent reported some form of musculoskeletal complaint, defined as experiencing pain, stiffness or aching in or around a joint during the past 30 days. Compared with survivors with no reported musculoskeletal complaints, individuals reporting one or more complaints reported significantly more bodily pain and lower health status and function, based on sub-scales of the Medical Outcomes Study Short Form (SF-36) (p < 0.001). CONCLUSIONS: These results suggest that musculoskeletal complaints may be highly prevalent in a long-term TBI survivor population and confer significant pain and disability. Future studies should be directed at elucidating modifiable risk factors associated with the development of musculoskeletal disorders following TBI. In the meantime, attention should be paid to the assessment and long-term management of these complaints among TBI survivors.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".