Traumatic brain injuries among veterans and the risk of incident dementia: A systematic review & meta-analysis
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injuries (TBI) among military veterans are increasingly recognized as important causes of both short and long-term neuropsychological dysfunction. However, the association between TBI and the development of dementia is controversial. This systematic review and meta-analysis sought to quantify the risks of all-cause dementia including Alzheimer's diseases and related dementias (ADRD), and to explore whether the relationships are influenced by the severity and recurrence of head injuries. METHODS: Database searches of Medline, Embase, Ovid Healthstar, PubMed and PROSPERO were undertaken from inception to December 2020 and supplemented with grey literature searches without language restrictions. Observational cohort studies examining TBI and incident dementia among veterans were analysed using Dersimonian-Laird random-effects models. RESULTS: Thirteen cohort studies totalling over 7.1 million observations with veterans were included. TBI was associated with an increased risk of all-cause dementia (hazard ratio [HR] = 1.95, 95% confidence interval [CI]: 1.55-2.45), vascular dementia (HR = 2.02, 95% CI: 1.46-2.80), but not Alzheimer's disease (HR = 1.30, 95% CI: 0.88-1.91). Severe and penetrating injuries were associated with a higher risk of all-cause dementia (HR = 3.35, 95% CI: 2.47-4.55) than moderate injuries (HR = 2.82, 95% CI: 1.44-5.52) and mild injuries (HR = 1.91, 95% CI: 1.30-2.80). However, the dose-response relationship was attenuated when additional studies with sufficient data to classify trauma severity were included. CONCLUSION: TBI is a significant risk factor for incident all-cause dementia and vascular dementia. These results need to be interpreted cautiously in the presence of significant heterogeneity.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".