Neuropsychological Tests of Memory, Visuospatial, and Language Function in Parkinson's Disease: Review, Critique, and Recommendations
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment in Parkinson's disease (PD) is a key non-motor complication during the disease course. OBJECTIVES: A review of detailed cognitive instruments to detect mild cognitive impairment (PD-MCI) or dementia (PDD) is needed to establish optimal tests that facilitate diagnostic accuracy. METHODS: We performed a systematic literature review of tests that assess memory, language including premorbid intelligence, and visuospatial domains (for tests of attention and executive functions see accompanying review) to determine suitability to assess cognition in PD. Based on in-depth scrutiny of psychometric and other relevant clinimetric properties, tests were rated as "recommended," "recommended with caveats," "suggested," or "listed" by the International Parkinson and Movement Disorder Society (IPMDS) panel of experts according to the IPMDS Clinical Outcome Assessment Scientific Evaluation Committee guidelines. RESULTS: We included 39 tests encompassing 48 outcome measures. Seven tests (different versions or subtests of the test counted once) were recommended, including four for memory, one for visuospatial domains, one for language (including three measures), and one for estimated premorbid intelligence. Furthermore, 10 tests (12 measures) were "recommended with caveats," 11 were "suggested," and 11 (15 measures) were "listed." CONCLUSIONS: Recommended neuropsychological tests in memory, visuospatial functions, and language are proposed to guide the assessment of cognitive impairment and its progression in PD-MCI and PDD, and for use in clinical trials to stratify participants or as outcome measures. Novel measures being developed will need extensive validation research to be "recommended." © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".