0938 Decreases in REM Sleep Following Traumatic Brain Injury may Contribute to Chronic Morbidity
Bibliographic record
Abstract
We set out to determine if posttraumatic sleep was associated to long-term outcome. Sex, age and hormonal effects were evaluated as these are known to have a functional influence. Traumatic brain injured patients (n=24) were assessed via overnight polysomnography (PSG). The mean age was 45 years and mean latency from TBI was 163 days. Patient hormone levels were analyzed within 10 days of PSG. The primary outcome measures were as follows: California Verbal Learning Test (CVLT-II), Montreal Cognitive Assessment (MoCA), Trails, Beck Depression Inventory (BDI-II), and Neuro-QoL. Wake after sleep onset was negatively correlated with scores on the MoCA and Neuro-QoL ability to participate. In contrast, percent time spent in REM was positively correlated with scores on MoCA and participation. REM was also associated with list recall (p<0.05). Analysis of sex effects, indicated that women showed less % REM and scored lower on NeuroQoL cognitive and communication scales (p<0.05). Women also indicated more subjective fatigue (p<0.05) and showed a trend for lower sleep latency. A clinically significant apnea-hypopnea index (AHI) of ≥5 was observed in 33% of TBI patients. Apnea was not significantly correlated with body mass index. Hormonal analysis showed that male testosterone levels were negatively correlated with N2% and positively correlated N3%. Levels of thyroxine were negatively correlated with REM% and Sleep Efficiency. The Epworth sleepiness scale was not associated with PSG sleep variables. A lower score on the sleep NeuroQoL subscale was however associated with a higher AHI. Decreases in REM sleep were associated with poorer cognitive performance and may ultimately affect functional outcome. Sex differences should be considered when evaluating sleep after TBI. The use of subjective sleep measures is questionable following TBI. Posttraumatic apnea could be indicative of TBI-related autonomic disturbances. none
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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 | 0.000 | 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) | 0.003 | 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".