Total daily sleep duration and the risk of dementia: a prospective population‐based study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We determined in a population-based study whether sleep duration was associated with an increased risk of incident dementia. METHODS: In a population-based study in central Spain, participants were evaluated at baseline and 3 years later. Baseline demographic variables were recorded and participants indicated their daily sleep duration as the sum of night-time sleep and daytime napping. The average daily total sleep duration was grouped into five categories: < or = 5 (short sleepers), 6, 7 (reference), 8, and > or = 9 h (long sleepers). We identified all cases with incident dementia, diagnosed using DSM-IV criteria. RESULTS: Three thousand two hundred eighty six participants with baseline information about sleep duration had a median duration of follow-up of 3.2 years. There were 140 incident cases of dementia. The relative risks (RR) for short sleepers and for long sleepers were 2.36 (95% CI = 1.07-5.21, P = 0.03) and 2.40 (95% CI = 1.20-4.81, P = 0.01), respectively. After adjustment for potential confounders, the RR was only marginally increased for short sleepers (1.87, 95% CI = 0.85-4.15, P = 0.12) but remained increased for long sleepers (2.18; 95% CI = 1.09-4.37, P = 0.03). CONCLUSIONS: Prolonged sleep duration (night-time sleep and daytime napping) may be associated with an increased risk of dementia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".