Quantitative EEG as a diagnostic and prognostic tool in hemispheric stroke patients undergoing type A aortic dissection surgery
Bibliographic record
Abstract
OBJECTIVE: The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) parameters, specifically the symmetry of amplitude-integrated electroencephalography (aEEG) and relative band power (RBP), in the postoperative stroke of the cerebral hemisphere following type A aortic dissection, remains an area of inquiry. METHODS: We analyzed and processed 56 patients with type A aortic dissection who underwent bedside qEEG monitoring and analyzed the qEEG indices, brain CT, and clinical data of these patients. qEEG (symmetry of aEEG and RBP, and affected/unaffected hemisphere) indices were analyzed at discharge and 60 days after discharge. RESULTS: A total of 56 patients were studied. The 60-day mortality rate was 12.5%. The affected hemisphere's diagnosis and mortality after 1-year follow-up were evaluated, and RBP beta demonstrated the highest area under the curve values with 95% confidence intervals (CI) of .849 (95% CI: .771-.928) and .91 (95% CI: .834-.986), respectively. According to the results of the logistic regression analysis, we have identified the strongest predictors for cerebral hemisphere stroke and 1-year mortality in stroke patients. Specifically, aEEGmin exhibited the highest predictive power with an odds ratio (OR) of .735 for cerebral hemisphere stroke, whereas DTABR was confirmed as one of the strongest predictors with an OR of 1.619 for 1-year mortality in stroke patients, indicating a high level of reliability. Spearman correlation coefficients showed that aEEGmax and aEEGmin were positively correlated with Alberta Stroke Program Early CT Score (aEEGmax: rho = .50, p < .001; aEEGmin: rho = .44, p < .001). CONCLUSIONS: QEEG has been proven to be a sensitive indicator for monitoring brain function and can be monitored continuously. It can help clinicians detect and treat these patients early and improve long-term prognosis.
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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 | 0.000 | 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".