Sleep in the Acute Phase of Severe Traumatic Brain Injury
Bibliographic record
Abstract
Background and Objectives The onset of pervasive sleep-wake disturbances associated with traumatic brain injury (TBI) is poorly understood. This study aimed to (a) determine the feasibility of using polysomnography in patients in the acute, hospitalized stage of severe TBI and (b) explore sleep quality and sleep architecture during this stage of recovery, compared to patients with other traumatic injuries. Methods A cross-sectional case-control design was used. We examined the sleep of 7 patients with severe TBI (17-47 years; 20.3 ± 15.0 days postinjury) and 6 patients with orthopedic and/or spinal cord injuries (OSCI; 19-58 years; 16.9 ± 4.9 days postinjury). One night of ambulatory polysomnography was performed at bedside. Results Compared to OSCI patients, TBI patients showed a significantly longer duration of nocturnal sleep and earlier nighttime sleep onset. Sleep efficiency was low and comparable in both groups. All sleep stages were observed in both groups with normal proportions according to age. Conclusion Patients in the acute stage of severe TBI exhibit increased sleep duration and earlier sleep onset, suggesting that the injured brain enhances sleep need and/or decreases the ability to maintain wakefulness. As poor sleep efficiency could compromise brain recovery, further studies should investigate whether strategies known to optimize sleep in healthy individuals are efficacious in acute TBI. While there are several inherent challenges, polysomnography is a useful means of examining sleep in the early stage of recovery in patients with severe TBI.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".