P111 Sleep disturbances are associated with poor neurobehavioural outcomes following traumatic brain injury: A study of military service members and veterans
Bibliographic record
Abstract
Abstract Introduction Sleep disturbances are pervasively reported in military service members and veterans, especially following traumatic brain injury (TBI). The purpose of this study was to examine the association between sleep disturbances and neurobehavioural outcomes in a large group of U.S. military service members and veterans, with and without a history of TBI. Methods Participants were enrolled into the Defense and Veterans Brain Injury Center/Traumatic Brain Injury Center of Excellence, 15-Year Longitudinal TBI study (N = 606). Participants self-reported sleep disturbances (PROMIS 8A) and neurobehavioral symptoms. Data were analyzed using analysis of variance with post-hoc comparisons. Four groups were analyzed separately: uncomplicated mild TBI (MTBI; n=218); complicated mild, moderate, severe, or penetrating - combined TBI (CTBI; n=118); injured controls (IC, i.e., orthopedic or soft-tissue injury without TBI; n=162); and non-injured controls (NIC; n=108). Results Participants in the MTBI group reported the highest proportion of moderate-severe sleep disturbances (66.5%) compared to the IC (54.9%), CTBI (47.5%), and NIC groups (34.3%). Participants classified as having Poor Sleep reported significantly worse scores on almost all TBI-QOL scales compared to those classified as having Good Sleep, regardless of TBI severity or even the presence of TBI (ps<.05, Cohen’s ds>.3). Discussion This study demonstrates that sleep disturbances remain a prevalent and debilitating concern in service member and veteran populations. Regardless of group (injured or NIC), sleep disturbances were common and were associated with significantly worse neurobehavioral functioning. When assessing and treating neurobehavioural symptoms, it is important to assess sleep, especially in service member and veteran populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".