Characterizing Sleep and Wakefulness in the Acute Phase of Concussion in the General Population: A Naturalistic Cohort from the Toronto Concussion Study
Bibliographic record
Abstract
Growing literature links concussion to changes in sleep and wakefulness in humans and in rodent models. Sleep has been linked with synaptic reorganization under other conditions; however, the characterization and role of sleep after acute concussion remains poorly understood. While much research has focused on insomnia among patients with chronic or persistent concussion symptoms, there is limited understanding of sleep and acute concussion, its potential role in recovery, and associated risk factors for the development of chronic sleep disturbance. Studies to date are limited by small sample sizes of primarily athlete or military populations. Additional studies among the general population are critical to inform best practice guidelines. We examined the sleep and daytime wakefulness of 472 adults from a naturalistic general population cohort (mean age, 33.3 years, females = 60.8%) within seven days of diagnosed concussion, using a validated, condition-specific measure, the Sleep and Concussion Questionnaire. Participants identified immediate changes in sleep characterized by hypersomnia and difficulty maintaining daytime wakefulness; 35% considered these changes as moderate to severe and 79% required monitoring or follow-up. Females experienced significantly greater severity of changes in sleep compared with males. Positive correlations between severity of sleep and pain and headache were identified. Differences by sex are an important consideration for early intervention and long-term monitoring. Because sleep was compromised by pain, pain management is also an integral part of early intervention. Our findings suggest that assessment of sleep beginning in the acute stage is a critical component of concussion management in the general 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".