794 Contribution of sleep disorders to dementia incidence following traumatic brain injury: A decade-long retrospective cohort study
Bibliographic record
Abstract
Abstract Introduction We aimed to examine the association between sleep disorders and dementia risk in a population-based cohort of adult male and female patients with traumatic brain injury (TBI). Methods We studied a province-wide retrospective cohort of all adult patients (≥ 18 years) free of dementia at the admission to the emergency department or acute care hospital with diagnoses of TBI between May 2003 and April 2013. All patients were followed through until May 2016. The primary exposure was a sleep disorder, and the primary outcome was dementia, both defined by the International Classification of Diseases, tenth revision diagnosis. Associations of sleep disorders with dementia were analyzed in multivariate Cox Proportional Hazard modeling. Results In total, 712,708 patients with TBI of all severities were included in this study. Their median age was 44 years, 59% were males. Over a median follow-up of 52 months (interquartile range, 19–86 months), 32,834 (4.6%) developed dementia. Controlling for age, sex, income level, injury severity, and known comorbidity risks, diagnosed sleep disorder was a significant predictor of incident dementia: hazard ratio (HR), 1.250 [95% CI, 1.146–1.363]. When results were stratified by sex, the association of sleep disorder with dementia remained significant in male: HR 1.255 [95% CI, 1.112–1.415] and in female patients: HR 1.234 [95% CI, 1.088–1.400]. Sensitivity analyses on Alzheimer’s disease case definition and using Fine and Gray competing risk models confirmed the association between sleep disorder and dementia in both sexes. Conclusion In both sexes, sleep disorders were independently associated with dementia onset (adjusted HRs>1.2). Thus, screening for sleep disorders should be part of regular care for TBI patients, as with the steady increase of TBI survivorship and life expectancy, undiagnosed sleep disorders can initiate a new cascade of cognitive deficits independent from TBI. Support (if any) This work was supported by the postdoctoral research grant from the Alzheimer’s Association (AARF-16-442937) to T.Mollayeva. The authors were also supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R21HD089106 and the Canadian Institutes for Health Research Grant–Institute for Gender and Health (#CGW-126580).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".