Natural history of excessive daytime sleepiness: a population-based 5-year longitudinal study
Bibliographic record
Abstract
STUDY OBJECTIVES: To document the rates of persistent, remitted, and intermittent excessive daytime sleepiness (EDS) in a longitudinal 5-year community study of adults and to assess how changes in risk factors over time can predict improvement of daytime sleepiness (DS). METHODS: Participants were recruited in 2007-2008 as part of a population-based epidemiological study implemented in Canada. They completed postal assessments at baseline and at each yearly follow-up. An Epworth Sleepiness Scale total score >10 indicated clinically significant EDS; a 4-point reduction between two consecutive evaluations defined DS improvement. Socio-demographic, lifestyle, health characteristics, and sleep-related measures (e.g. insomnia symptoms, sleep duration, sleep medication) were self-reported at each time point. Cox proportional-hazard models were used to predict EDS and DS remissions over 5 years. RESULTS: Among the 2167 participants, 33% (n = 714) met criteria for EDS at baseline, of whom 33% had persistent EDS, 44% intermittent EDS, and 23% remitted EDS over the follow-up. Furthermore, 61.4% of 2167 initial participants had stable DS, 27.1% sustained DS improvement and 8.5% transient improvement over the follow-up. The main predictors of EDS remission or DS improvement were normal weight, taking less hypnotics, having hypertension, increased nighttime sleep duration, and decreased insomnia, and depressive symptoms. CONCLUSIONS: EDS waxes and wanes over time with frequent periods of remission and is influenced by behavioral characteristics and changes in psychological, metabolic, and nighttime sleep patterns. Targeting these predictors in future interventions is crucial to reduce DS in the general adult population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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".