Remote Traumatic Brain Injury Is Associated with Motor Dysfunction in Older Military Veterans
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) has been identified as a risk factor for Parkinson's disease (PD). Motor dysfunction among TBI-exposed elders without PD has not been well characterized. We sought to determine whether remote TBI is a risk factor for motor dysfunction on exam and functionally relevant motor dysfunction in day-to-day life among independently living elders without PD. METHODS: This is a cross-sectional cohort study of independently living retired military veterans aged 50 or older with (n = 78) and without (n = 85) prior TBI-all without diagnosed PD. To characterize multidimensional aspects of motor function on exam, the Unified Parkinson's Disease Rating Scale (UPDRS) Motor Examination was performed by a board-certified neurologist and used to calculate a modified UPDRS (mUPDRS) global motor score and four domain scores (tremor, rigidity, bradykinesia, and posture/gait). Functionally relevant motor dysfunction was assessed via self-report of falls within the past year. RESULTS: In analyses adjusted for demographics and comorbidities that differed between groups, compared with veterans without TBI, those with moderate-to-severe TBI were more likely to have fallen in past year (33% vs. 14%, risk ratio 2.5 [95% confidence interval 1.1-5.4]), had higher (worse) mUPDRS global motor (p = .03) and posture/gait scores (p = .02), but not higher tremor (p = .70), rigidity (p = .21), or bradykinesia scores (p = .22). Mild TBI was not associated with worse motor function. CONCLUSIONS: Remote moderate-to-severe TBI is a risk factor for motor dysfunction-defined as recent falls and impaired posture/gait-among older veterans. TBI-exposed older adults may be ideal candidates for aggressive fall-screening and prevention strategies.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".