Quantitative EEG Spectral Power Ratio As Cognitive Biomarker For Patients With Parkinson Disease
Bibliographic record
Abstract
Background: Cognitive decline in patients with Parkinson disease (PD) is a major and progressing health problem that needs reliable and objective assessment tools. Aim: Toexplore the value of EEG spectral ratio as cognitive biomarker in patients with PD. Methods: This cross-sectional case control study enrolled 35 patients with PD and 20 matched healthy controls. All participants were evaluated by quantitative electroencephalography (EEG) spectral power ratio (slow/fast) over different head regions, in addition to clinical and neuropsychological assessment of the patients using Unified Parkinson’s Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA). Results: The UPDRS score of the patients was (mean 46.8 ± SD 26.6) and total MoCA score was (mean 20.3 ± SD 5.7). Twenty four of PD patients had cognitive impairment (MoCA <26) and showed significant higher spectral power ratio over the occipital region compared to PD patients with normal cognition (P=0.028). No significant differences of spectral power ratio between PD patients and controls. No significant correlation was found between power spectral ratio, UPDRS and MoCA scores. Conclusions: The occipital EEG spectral power ratio could be used as a complementary tool to neuropsychological assessment in evaluation and follow up of cognitive decline in patients with PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".