Bibliographic record
Abstract
Cerebral hemorrhage may cause cognitive dysfunction. Electroencephalogram (EEG) is a noninvasive diagnostic tool for assessment of cerebral function. A total of 174 patients (including cognitively impaired and cognitively normal) with cerebral hemorrhage, and 120 healthy persons (CN), were recruited between August 2008 and July 2012 at the Department of Neurology. EEG was used to analyze cerebral function of patients and normal persons. Correlation, clustering and concordance analyses were performed to analyze the relationship between EEG power and Montreal Cognitive Assessment (MoCA) scores. Cognitively impaired patients had a significantly decreased EEG beta power (0.793 ± 0.176 µV(2)) compared with cognitively normal patients (1.589 ± 0.205 µV(2), P < .01) or healthy persons (1.651 ± 0.185 µV(2), P < .01). Significantly negative correlations between beta power and hemorrhage region, size, amount, and patients' age were apparent (r = -0.91888, -0.78569, -0.84961, and -0.80365, respectively, all Ps < .001). There was good concordance between the K-means clustering algorithm calculating beta power and MoCA scoring (κ = .904, P < .001). In conclusion, the analysis method of EEG (beta power) abnormalities holds considerable promise to assess cognitive impairment after cerebral hemorrhage. Cognitive impairment was negatively correlated to hemorrhage region, size, amount, and age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".