Circadian rhythm in the assessment of postconcussion insomnia: a cross-sectional observational study
Bibliographic record
Abstract
BACKGROUND: Insomnia is a major predictor of adverse outcomes in mild traumatic brain injury (mTBI), including concussion; although insomnia symptoms may be due to various sleep disorders, those related to circadian rhythm sleep-wake disorders (CRSWDs) require specific assessment and treatment. The objective of the current study was to determine the prevalence of CRSWD in a sample of treatment-seeking people with chronic insomnia symptoms after an mTBI. METHODS: Participants aged 17-65 years who had experienced an mTBI and reported chronic insomnia were recruited from diverse community clinics in Ontario 3-24 months after their injury to participate in this cross-sectional observational study. Potential participants were screened by both telephone and intake interview. Exclusion criteria were alcohol or substance use disorders, preexisting brain disorder or previous neurosurgery, recent travel across more than 2 time zones or shift work. Assessments included a clinical interview, questionnaires, 2 weeks of actigraphy and a sleep diary, and a dim-light melatonin onset test. The main outcome measure was the proportion of patients with CRSWDs. RESULTS: Of the 50 participants (32 [64%] female; median age 39.5 yr), 13 (26% [standard deviation 12%]) had an CRSWD. The most common circadian diagnosis was delayed sleep-wake phase disorder (10 participants [20%]). INTERPRETATION: The prevalence of CRSWDs may be exceptionally high among people with chronic insomnia symptoms following mTBI. Proper detection and treatment of CRSWDs in this population is essential to facilitate recovery. The findings emphasize the relevance of a diagnostic circadian assessment in patients with mTBI presenting with chronic insomnia symptoms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".