Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) and the Dementia Rating Scale-2 (DRS-2) are recommended screeners for Parkinson's disease mild cognitive impairment (PD-MCI). Cross-cultural studies examining their diagnostic precision have not addressed cultural bias in a multicultural setting. OBJECTIVES: To compare DRS-2 and MoCA performance between patients born in Canada, the USA, and the UK (Anglosphere group) and immigrant patients born elsewhere (International group). To identify sources of cultural bias by comparing group characteristics, and by assessing the relationships between performance and immigration and socio-development variables. To examine the diagnostic precision of both tools in detecting PD-MCI in each group. METHODS: We conducted a clinical chart review of advanced PD patients who completed cognitive screeners (MoCA: n = 288, 30% International group; DRS-2: n = 426, 31% International group). All completed a comprehensive neuropsychological assessment to apply Level II PD-MCI diagnostic criteria. RESULTS: The International group performed worse than the Anglosphere group on the MoCA and DRS-2, and the only variable that accounted for some of the group difference was the Historical Index of Human Development, a societal variable, which fully mediated the group effect on the DRS-2. Diagnostic precision of the MoCA was at chance level in the International group, and was poorer than that of the DRS-II in this group and that of the MoCA in the Anglosphere group, although these were considered poor. CONCLUSIONS: Our results support the recommendation to exert caution in using cognitive screeners to capture PD-MCI in all patients and particularly with first generation immigrants.
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 | 0.061 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".