Sleep Apnea in Traumatic Brain Injury: Understanding Its Impact on Executive Function
Bibliographic record
Abstract
Background: Persons who have sustained a traumatic brain injury are at a significantly increased risk for sleep disorders. One of the most commonly diagnosed sleep disorders after traumatic brain injury is sleep apnea, defined as a cessation of breathing accompanied by frequent arousals and hypoxia during sleep. The effects of untreated sleep apnea on a person’s cognitive decline and the development of behavioral deficits have only recently been identified. It has been shown that axonal damage can occur because of sleep apnea and numerous neuropsychological studies of sleep apnea patients show deficits in cognitive domains, such as executive function and attention. However, there has been little published discussion regarding the interaction between sleep apnea and executive function among persons with traumatic brain injury. Objectives: The objectives of this review were to 1) review/synthesize published work relevant to the discussion of sleep apnea influencing executive function; and 2) clarify the nature of the interface between executive function and sleep apnea in persons with traumatic brain injury. Results: Until now, little attention has been directed to the neurobehavioral consequences of sleep apnea in persons with traumatic brain injury. There is an urgent need for more longitudinal research examining the effects of sleep apnea on executive function after traumatic brain injury and the effectiveness of sleep apnea treatment on executive function after injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".