Assessing Cognitive Impairment in Quechua and Aymara Patients: A Critical Examination of the Rowland Universal Dementia Assessment Scale’s Discriminative Capacity
Bibliographic record
Abstract
BACKGROUND: The global aging population raises concerns about increased neurodegenerative diseases, particularly in low- and middle-income countries like Latin America and the Caribbean. However, the situation among the indigenous inhabitants remains unknown due to various barriers, including cultural diversity, lack of studies, low awareness, language barriers, and limited healthcare access. Brief cognitive tests like the Rowland Universal Dementia Assessment Scale (RUDAS) show promise in overcoming these challenges. METHOD: A secondary analysis was conducted on a substantial patient cohort derived from the GAPP study (NIA grant #alz093273AG069118), categorizing participants based on whether their native language was Spanish or Quechua/Aymara. Descriptive analysis of variables was performed, with the Wilcoxon test applied to compare total RUDAS scores among distinct patient groups, including controls, individuals with Mild Cognitive Impairment (MCI), and those with dementia. Comparisons were specifically made between Spanish speakers and native language speakers. Additionally, the ROC (Receiver Operating Characteristic) curve was employed to evaluate the discriminative capacity of RUDAS in distinguishing between controls and dementia, as well as between controls/MCI and dementia cases within native and Spanish-speaking populations. RESULT: A total of 405 controls, 126 individuals with Mild Cognitive Impairment (MCI), and 133 participants with dementia were included in the analysis. Among them, 91 patients were native language speakers, with a median age of 74 (range 54 - 92). The majority were female (69.2%), and 65.9% were Aymara speakers. Native participants reported an average education level of 3.1 years (SD 4.4). The RUDAS mean score was lower in the native speaker group compared to Spanish-speaking participants (21 vs. 23). Additionally, the analysis revealed significantly lower scores in controls within the native speaker group (Fig. 1). Diagnostic performance for native speakers was poor when comparing control/MCI (Fig. 2) vs. dementia and control vs. dementia (Fig. 3). CONCLUSION: The use of the RUDAS in native language speakers and its ability to distinguish between Mild Cognitive Impairment (MCI) and dementia raise some concerns. To improve assessments for various communities in Latin America and the Caribbean, there is a need for culturally adapted brief cognitive tests.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".