Educational Level as a Modulator of Cognitive Performance and Neuropsychyatric Features in Parkinson Disease
Bibliographic record
Abstract
OBJECTIVES: To test a possible association between the educational level (EL), cognitive performance, and neuropsychiatric features in Parkinson disease (PD). BACKGROUND: An inverse association has been reported between EL and cognitive dysfunction in patients with senile dementia of Alzheimer type but it is yet unsettled whether education has a similar effect on cognition in PD. METHODS: Seventy-two PD patients (45 males, mean age 68.7+/-11.6 y) underwent a detailed neurologic examination, a battery of neuropsychologic tests, and questionnaires for the evaluation of psychosis, sleep disturbances, and depression. According to the number of educational years, patients were divided into 3 groups: low EL (0 to 8 y), (15 patients), intermediate EL (9 to 12 y) (28 patients), and high EL (>/=13 y) (29 patients). RESULTS: Patients with a higher EL had a better cognitive function and an association was found between the patients' EL and their scores in various neuropsychologic tests mainly those sensitive to frontal lobe dysfunction. Low education was associated with an increased risk for hallucinations and a trend for more depression, delusions, and sleep disturbances. CONCLUSIONS: The association between high educational attainment and the lower risk of cognitive dysfunction suggest that education might modulate cognitive performance in 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 | 0.003 |
| 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 | 0.001 | 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".