Cognitive testing in late-stage Parkinson's disease: A critical appraisal of available instruments
Bibliographic record
Abstract
For patients with Parkinson’s disease (PD), cognitive impairment is one of the most disabling non-motor symptoms, particularly in the late disease stages (LSPD). Without a common cognitive assessment battery, it is difficult to estimate its prevalence and limits comparisons across studies. In addition, some instruments traditionally used in PD may not be adequate for use in LSPD. We sought to identify instruments used to assess cognition in LSPD and to investigate their global characteristics and psychometric properties to recommend a cognitive battery for the LSPD population. We conducted a literature search of EMBASE and MEDLINE for articles reporting the use of cognitive tests in LSPD. The global characteristics and psychometric properties of the four most used cognitive tests in each cognitive domain were verified to recommend a cognitive assessment battery. Of 60 included studies, 71.7% used screening scales to assess cognition. Of the 53 reported instruments, the Montreal Cognitive Assessment, the Digit Span, the Trail Making Test, the Semantic Fluency test, the Rey Auditory Verbal Learning Test, the Brief Visuospatial Memory Test-Revised, the Boston Naming Test, the Judgment of Line Orientation, and the Clock Drawing Test corresponded best overall to the requirements considered important for selecting instruments in LSPD. Screening scales are frequently used to assess cognition in LSPD. We recommend a cognitive assessment battery that considers the special characteristics of the LSPD population, including being quick and easy to use, with minimized motor demands, and covering all relevant cognitive domains.
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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 | 0.052 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".