B.4 Quantitative electroencephalography to predict post-stroke disability: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: We aim to assess the role of quantitative electroencephalography (QEEG) derived indices to predict post-stroke disability. Methods: We included observational studies (sample-size≥10) of patients with stroke who underwent EEG and a follow-up outcome assessment was available either in form of a modified Rankin scale (mRS) or National Institute of stroke scale (NIHSS) or Fugl-Meyer scale (FMA). QEEG indices analyzed were delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry (BSI) and pairwise derived brain symmetry (pdBSI). Results: Twelve studies (11 had only ischemic stroke, and one had both ischemic and hemorrhagic stroke), including 513 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=172, r 0.49, 95% CI 0.31-0.67). pdBSI was inversely associated with FMA (n=20, r-0.50 95% CI -0.86-(-0.14)) and BSI was not associated with FMA (n=21, r -0.3 95% CI -0.81-0.22). Conclusions: QEEG-derived indices have the potential to assess post-stroke disability. Adding QEEG to the clinical and imaging biomarkers may help in better prediction of post-stroke recovery. PROSPERO 2022 CRD42022292281
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".