Self-report instruments for assessing sleep dysfunction in an adult traumatic brain injury population: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To review the number and characteristics of self-reported sleep measures used to evaluate impaired sleep/wakefulness in traumatic brain injury (TBI) populations. METHODS: We conducted a comprehensive peer-reviewed literature search of Medline, Embase, PsycINFO, CINAHL, and various bibliographies. Only standardized self-report measures for evaluating sleep dysfunction and its signs were taken into consideration. RESULTS: Sixteen self-report measures used in TBI research and clinical practices were identified. Five were generic, five symptom-related, and six were condition-specific measures. The Pittsburgh sleep quality index and Epworth sleepiness scale were partially validated in post-acute TBI. CONCLUSION: Although no instrument has been specifically developed for TBI patients, there are scientific benefits to using the existing measures. However, additional research is needed to examine their applicability to the TBI population. The design and introduction of a new instrument able to triage sleep-related complaints between depressive, other medical, and primary sleep disorders-with a section for caregiver reports-might assist in the identification of the etiology of sleep dysfunction in persons with TBI. In choosing or developing a sleep measure, researchers and clinicians must consider the specific domains they want to screen, diagnose, or monitor. Polysomnography is recommended for diagnosing specific sleep disorders that cannot be diagnosed solely using a self-report measure.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".