Quantitative electroencephalography to assess post-stroke functional disability: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Quantitative electroencephalography (QEEG) is a non-invasive, reliable and easily accessible modality to assess neuronal activity. QEEG in acute stroke may predict short and long-term functional outcomes. The role of individual indices has not been studied in a meta-analysis. We aim to assess individual QEEG-derived indices to predict post-stroke disability. METHODS: We included studies (sample size ≥ 10) with stroke patients who underwent EEG and a follow-up outcome assessment was available either in the form of modified Rankin scale (mRS) or National Institute of Stroke scale (NIHSS) or Fugl-Meyer scale (FMA). QEEG indices analysed were delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry index (BSI) and pairwise derived brain symmetry (pdBSI). RESULTS: Nine studies (8 had only ischemic stroke, and one had both ischemic and haemorrhagic stroke), including 482 participants were included for meta-analysis. Higher DAR was associated with worse mRS (n=300, Pearson's r 0.26, 95 % CI 0.21-0.31). Higher DTABR was associated with worse mRS (n=337, r=0.32, 95 % CI 0.26-0.39). Higher DAR was associated with higher NIHSS (n=161, r=0.42, 95 % CI0.24-0.6). Higher DTABR was associated with higher NIHSS (n=158, r=0.49, 95 % CI 0.31-0.67). CONCLUSIONS: QEEG-derived indices DAR and DTABR have the potential to assess post-stroke disability. Adding QEEG to the clinical and imaging biomarkers in the acute phase may help in better prediction of post-stroke recovery. REGISTRY: PROSPERO 2022 CRD42022292281.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".