Impaired Percent Alpha Variability on Continuous Electroencephalography Is Associated with Thalamic Injury and Predicts Poor Long-Term Outcome after Human Traumatic Brain Injury
Bibliographic record
Abstract
Continuous electroencephalography (cEEG) is potentially useful in determining prognosis in patients with traumatic brain injuries (TBI). The objective of this prospective, observational cohort study was to determine if the percent alpha variability (PAV) on cEEG was predictive of outcome following TBI. Injury characteristics were indexed to assess whether lesions in specific cerebral loci were correlated with PAV and patient recovery. Fifty-three TBI patients were studied using cEEG recording and serial neuroimaging. Clinical recovery was assessed at regular intervals in hospital and following discharge. The principal outcome measures included the mean 3-day PAV score, the 7-day PAV pattern, delineation of the anatomical sites of brain injury, and the 6-month clinical outcome, as measured by the Glasgow Outcome Scale (GOS). Significant univariate (p = 0.030) and multivariate (p = 0.008) relations were identified between PAV and GOS scores. PAV offered good discrimination between favorable and unfavorable 6-month outcomes (AUC 0.76) and, with a cutpoint of 0.20, had a sensitivity of 87% and negative predictive value of 82%. Multivariate modeling revealed that injuries of the thalamus (p = 0.009) and basal ganglia (p = 0.016), and the presence of diffuse edema (p = 0.009), were the key anatomical predictors of PAV. Brainstem injuries (p = 0.020) and indicators of diffuse cerebral trauma, such as deep white matter shearing (p = 0.036) and multiple subcortical lesions (p = 0.033), were the principal determinants of 6-month recovery. Inclusion of PAV enhanced the accuracy of prediction models that encompassed a selective combination of clinical and anatomical variables (adjusted R(2) = 0.458, p < 0.001). The two main results of this study are (1) PAV is a sensitive predictor of 6-month clinical outcomes following TBI, and (2) injury to the thalamus is related to impaired PAV. PAV appears best utilized as a functional adjunct to traditional clinical and anatomical predictors.
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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 | 0.003 |
| 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 | 0.001 | 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".