Evolution of severe sleep-wake cycle disturbances following traumatic brain injury: a case study in both acute and subacute phases post-injury
Bibliographic record
Abstract
BACKGROUND: Sleep-wake disturbances are frequently reported following traumatic brain injury (TBI), but they remain poorly documented in the acute stage of injury. Little is known about their origin and evolution. CASE PRESENTATION: This study presents the case of a patient in the acute phase of a severe TBI. The patient was injured at work when falling 12 m into a mine and was hospitalized in the regular wards of a level I trauma centre. From days 31 to 45 post-injury, once he had reached a level of medical stability and continuous analgosedation had been ceased, his sleep-wake cycle was monitored using actigraphy. Results showed significant sleep-wake disturbances and severe sleep deprivation. Indeed, the patient had an average nighttime sleep efficiency of 32.7 ± 15.4 %, and only an average of 4.8 ± 1.3 h of sleep per 24-h period. After hospital discharge to the rehabilitation centre, where he remained for 5 days, the patient was readmitted to the same neurological unit for paranoid delusions. During his second hospital stay, actigraphy recordings resumed from days 69 to 75 post-injury. A major improvement in his sleep-wake cycle was observed during this second stay, with an average nighttime sleep efficiency of 96.3 ± 0.9 % and an average of 14.1 ± 0.9 h of sleep per 24-h period. CONCLUSION: This study is the first to extensively document sleep-wake disturbances in both the acute and subacute phases of severe TBI. Results show that prolonged sleep deprivation can be observed after TBI, and suggest that the hospital environment only partially contributes to sleep-wake disturbances. Continuous actigraphic monitoring may prove to be a useful clinical tool in the monitoring of patients hospitalized after severe TBI in order to detect severe sleep deprivation requiring intervention. The direct impact of sleep-wake disturbances on physiological and cognitive recovery is not well understood within this population, but is worth investigating and improving.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".