Brain Changes: Non-Pharmacological Interventions to Improve Sleep Following Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury is an injury caused by external forces to the brain and often results in a sequalae of symptoms that span physical, emotional, and cognitive domains. Sleep disturbances are a commonly reported symptom of traumatic brain injury and refer to any disruption that affects the quality, quantity, timing or initiation of sleep. The objective of this paper was to conduct a preferred reporting items for systematic reviews and meta-analyses (PRISMA) extension for a scoping review to assess the existing evidence surrounding non-pharmaceutical interventions that affect sleep outcomes for traumatic brain injury survivors across the age and severity spectrums. Three electronic databases (Web of Science, PubMed, PsychInfo) were searched by three authors, using key words delineated from "Traumatic brain injury," "Sleep," and "Intervention." Of the 1486 papers originally identified in the search terms, 43 met the inclusion criteria and are included in this review. Non-pharmaceutical interventions (such as cognitive behavioral therapy for insomnia, light therapy, and exercise) seem to improve subjective sleep outcomes and quality of life across injury severities making it an important lifestyle target for intervention. However, the heterogeneity of outcome measures reported and small sample sizes as well as the lack of identification of the type of sleep disturbance addressed by an intervention limits the ability to make recommendations about the best intervention to enhance sleep outcomes following traumatic brain injury. Findings from this review emphasize the need for standardized assessment tools, longer-term studies, and increasing the number of studies including objective sleep tracking tools.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".