Describing the nature and extent of musculoskeletal complaints 15 or more years post-injury in a TBI survivor populations
Bibliographic record
Abstract
As the life expectancy following traumatic brain injury (TBI) increases, there is a growing need to address the secondary and tertiary complications that may arise numerous years after the initial injury. This study examined the nature and extent of musculoskeletal complaints in a TBI survivor population 15 or more years post-injury. Injury information from the initial TBI and follow-up was obtained for 34 individuals through medical record review and a telephone interview. Descriptive frequencies of the study population were generated and scores from the SF-36 were analyzed in relation to injury variables. There was a high incidence (79%) of reported musculoskeletal complaints after TBI, more than half the reported incidence of the general population in Ontario (Badley et al, 1995). Additionally, several interesting patterns of pain became evident within the study population including widespread, widespread sporadic, hemiplegic, isolated regional and upper body with hip pain distributions. Further, results from this study suggest that the impact of musculoskeletal complaints amongst TBI survivors is manifested in additional impairment as measured by the domains of the SF-36.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".