Differences and contributors to global cognitive performance in the underrepresented Latinx Parkinson’s disease population
Bibliographic record
Abstract
Objective: Despite significant progress in understanding the factors influencing cognitive function in Parkinson’s disease (PD), there is a notable gap in data representation for the Latinx population. This study aims to evaluate the contributors to and disparities in cognitive performance among Latinx patients with PD. Methods: A retrospective analysis was conducted based on cross-sectional data encompassing demographic, environmental, motor, and non-motor disease characteristics from the Latin American Research Consortium on the Genetics of PD (LARGE-PD) and the Parkinson’s Progression Markers Initiative (PPMI) cohorts. Linear regression multivariable models were applied to identify variables affecting Montreal Cognitive Assessment (MoCA) scores, accounting for age, sex, and years of education. Results: The analysis comprised of 3,054 PD patients (2,041 from LARGE-PD and 1,013 from PPMI) and 1,303 Latinx-controls. Latinx-PD patients (mean age 63.0 ± 11.8, 56.8% male) exhibited a significantly lower average MoCA score (p < .001) compared to white Non-Hispanic PD patients from PPMI (mean age 67.5 ± 9.9, 61.7% male). This difference persisted when comparing the Latinx-PD to the Latinx-controls (mean age 58.7 ± 9.3, 33.2% male; p < .001). Factors significantly associated with better MoCA scores in Latinx-PD included unilateral symptom onset (p = .009), and higher educational attainment (p < .001). Conversely, those associated with worse scores included the use of dopamine agonists (p = .01), previous tobacco use (p = .01), older age (p < .001), and a higher Hoehn and Yahr scale score (p < .001). Conclusions: Latinx-PD patients demonstrated significantly lower cognitive scores compared to their white non-Hispanic PD counterparts and Latinx-controls. These results highlight the importance of interpreting MoCA scores in a nuanced manner within diverse populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".