The effect of sleep disorders on dementia risk in patients with traumatic brain injury: A large‐scale cohort study
Bibliographic record
Abstract
Introduction: We investigated the association between sleep disorders (SDs) and incident dementia in adults with traumatic brain injury (TBI). Methods: Adults with a TBI between 2003 and 2013 were followed until incident dementia. Sleep disorders at TBI were predictors in Cox regression models, controlling for other dementia risks. Results: Over 52 months, 4.6% of the 712,708 adults (59% male, median age 44, <1% with SD) developed dementia. An SD was associated with a 26% and a 23% of increased risk of dementia in male and female participants (hazard ratio [HR] 1.26, 95% confidence interval [CI] 1.11-1.42 and HR 1.23, 95% CI 1.09-1.40, respectively). In male participants, SD was associated with a 93% increased risk of early-onset dementia (HR 1.93, 95% CI 1.29-2.87); this did not hold in female participants (HR 1.38, 95% CI 0.78-2.44). Discussion: In a province-wide cohort, SDs at TBI were independently associated with incident dementia. Clinical trials testing sex-specific SD care after TBI for dementia prevention are timely. Highlights: TBI and sleep disorders are linked to each other, and to dementia.It is unclear if sleep disorders pose a sex-specific dementia risk in brain injury.In this study, presence of a sleep disorder increased dementia risk in both sexes.The risk differed by type of sleep disorder, which differed between the sexes.Sleep disorder awareness and care in persons with brain injury is vital for dementia prevention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".