Rapid EEG Activity during Sleep Dominates in Mild Traumatic Brain Injury Patients with Acute Pain
Bibliographic record
Abstract
Chronic pain is a highly prevalent post-concussion symptom occurring in a majority of patients with mild traumatic brain injury (mTBI). About half of patients with mTBI report sleep-wake disturbances. It is known that pain can alter sleep quality in this population, but the interaction between pain and sleep is not fully understood. This study aimed to identify how pain affects subjective sleep (Pittsburgh Sleep Quality Index [PSQI]), sleep architecture, and quantitative electroencephalographic (qEEG) brain activity after mTBI. Twenty-four mTBI patients complaining of sleep-wake disturbances, with and without pain (8 and 16, respectively), were recruited 45 (±22.7) days post-trauma on average. Data were compared with those of 18 healthy controls (no sleep or pain complaints). The PSQI, sleep architecture, and qEEG activity were analyzed. Pain was assessed using questionnaires and a 100-mm visual analogue scale. Patients with mTBI reported three times poorer sleep quality than controls on the PSQI. Sleep architecture significantly differed between patients with mTBI and controls but was within normal range. Global qEEG showed lower delta (deep sleep) and higher beta and gamma power (arousal) at certain EEG derivations in patients with mTBI compared with controls (p<0.04). Patients with mTBI with pain, however, showed greater increase in rapid EEG frequency bands, mostly during REM sleep, and beta bands in non-REM sleep compared with patients with mTBI without pain and controls (p<0.001). Pain in patients with mTBI was associated with more rapid qEEG activity, mostly during REM sleep, suggesting that pain is associated with poor sleep and is a critical factor in managing post-concussion symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".