Detecting Mild Cognitive Deficits in <scp>P</scp>arkinson's <scp>D</scp>isease: <scp>C</scp>omparison of <scp>N</scp>europsychological <scp>T</scp>ests
Bibliographic record
Abstract
BACKGROUND: Numerous neuropsychological tests and test versions are used in Parkinson's disease research, but their relative capacity to detect mild cognitive deficits and their comparability across studies are unknown. The objective of this study was to identify neuropsychological tests that consistently detect cognitive decline in PD across studies. METHODS: Data from 30 normed neuropsychological tests across 20 international studies in up to 2908 nondemented PD patients were analyzed. A subset of 17 tests was administered to up to 1247 healthy controls. A 2-step meta-analytic approach using standardized scores compared performance in PD with normative data. RESULTS: Pooled estimates of the differences between PD and site-specific healthy controls identified significant cognitive deficits in PD patients on 14 test scores across 5 commonly assessed cognitive domains (attention or working memory, executive, language, memory, and visuospatial abilities), but healthy control performance was statistically above average on 7 of these tests. Analyses based on published norms only, as opposed to direct assessment of healthy controls, showed high between-study variability that could not be accounted for and led to inconclusive results. CONCLUSIONS: Normed neuropsychological tests across multiple cognitive domains consistently detect cognitive deficits in PD when compared with site-specific healthy control performance, but relative PD performance was significantly affected by the inclusion and type of healthy controls versus the use of published norms only. Additional research is needed to identify a cognitive battery that can be administered in multisite international studies and that is sensitive to cognitive decline, responsive to therapeutic interventions, and superior to individual cognitive tests. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".