Sleep from acute to chronic traumatic brain injury and cognitive outcomes
Bibliographic record
Abstract
STUDY OBJECTIVES: Traumatic brain injuries (TBIs) cause persistent cerebral damage and cognitive deficits. Because sleep may be a critical factor for brain recovery, we characterized the sleep of patients with TBI from early hospitalization to years post-injury and explored the hypothesis that better sleep during hospitalization predicts more favorable long-term cognitive outcomes. METHODS: We tested patients with moderate-to-severe TBI in the hospitalized (n = 11) and chronic (n = 43) stages using full-night polysomnography, with 82% of the hospitalized group being retested years post-injury. Hospitalized patients with severe orthopedic and/or spinal cord injury (n = 14) and healthy participants (n = 36) were tested as controls for the hospitalized and chronic TBI groups, respectively. Groups had similar age and sex and were compared for sleep characteristics, including slow waves and spindles. For patients with TBI, associations between sleep during hospitalization and long-term memory and executive function were assessed. RESULTS: Hospitalized patients with TBI or orthopedic injuries had lower sleep efficiency, higher wake after sleep onset, and lower spindle density than the chronic TBI and healthy control groups, but only hospitalized patients with brain injury had an increased proportion of slow-wave sleep. During hospitalization for TBI, less fragmented sleep, more slow-wave sleep, and higher spindle density were associated to more favorable cognitive outcomes years post-injury, while injury severity markers were not associated with these outcomes. CONCLUSION: These findings highlight the importance of sleep following TBI, as it could be a strong predictor of neurological recovery, either as a promoter or an early marker of cognitive outcomes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".