Poststroke QEEG informs early prognostication of cognitive impairment
Bibliographic record
Abstract
Cognitive impairment is a common consequence of stroke, but remains difficult to predict. We investigate the ability of early QEEG assessment to inform such prediction, using binary logistic regression. Thirty-five patients (12 female, ages 18-87) suffering middle cerebral artery, ischemic stroke were studied. Resting-state EEG was recorded 48-239 h after symptom onset. Relative power for delta, theta, alpha, and beta bands, delta:alpha ratio, and peak alpha frequency were analyzed. Montreal Cognitive Assessment (MoCA) was administered, where possible, on day of EEG and at median 99 days (range 69-138) poststroke. Eight patients could not complete the baseline MoCA, and four the follow-up MoCA, for varying reasons (most commonly, stroke symptoms). Fifteen patients (48%) had cognitive impairment (MoCA score ≤25) at follow-up. One QEEG index was able to correctly predict presence/absence of cognitive impairment in 24/31 patients (77.4%), whereas predischarge MoCA did so in 23 patients. This index, relative theta frequency (4-7.5 Hz) power, was computed from only three posterior electrodes over the stroke-affected hemisphere. Its predictive accuracy (three electrodes) was higher than that of any "global" QEEG measure (averaged over 19 electrodes). These results may signify association between poststroke alpha slowing and cognitive impairment, which may be mediated by attentional (dys)function, which warrants further investigation. Pending further studies, QEEG measure(s)-from a few electrodes-could inform early prognostication of poststroke cognitive outcomes (and clinical decisions), particularly when cognitive function cannot be adequately assessed (due to symptoms, language, or other issues) or when assessment is equivocal.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".