Quantitative electroencephalography in Alzheimer's disease: comparison with a control group, population norms and mental status.
Bibliographic record
Abstract
OBJECTIVE: Given that quantitative electroencephalography (EEG) has repeatedly shown excessive slow wave activity in dementia of the Alzheimer type (DAT) that increases with disease progression, we assessed the clinical utility of this tool by comparing various approaches used to assess slowing. DESIGN: Cross-sectional study comparing quantitative EEG data from patients with DAT with normative data from an elderly control group and from EEG norms derived from a large population. PARTICIPANTS: 35 subjects diagnosed with probable DAT and 30 elderly controls. OUTCOME MEASURE: EEG recorded from 21 scalp sites of each patient and elderly control during vigilance-controlled, eyes-closed, resting conditions was spectrally analyzed to yield measures of absolute and relative power in delta, theta, alpha and beta bands and indices of mean alpha band and total band frequency. RESULTS: Group comparisons of raw or age-regressed z-score population normative values yielded different profiles with respect to direction of frequency band changes, regional topography and clinical rating correlations, but both procedures evidenced overall patterns of EEG slowing in DAT. However, both methodologies yielded only modest (75%) classification rates. CONCLUSION: Quantitative EEG remains a valuable research tool but, as yet, an unproven diagnostic tool, for DAT.
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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) | 0.000 | 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".