Cognitive assessments in multicultural populations using the Rowland Universal Dementia Assessment Scale: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Canada has a growing multinational immigrant population. Many commonly used cognitive assessment tools have known cultural biases and are difficult to use in ethnically diverse patient populations. The Rowland Universal Dementia Assessment Scale (RUDAS) is a cognitive assessment tool that was created for culturally and linguistically diverse populations. We performed a systematic review and meta-analysis to assess the psychometric characteristics of the RUDAS and to compare it with other available tools. METHODS: We identified studies that assessed the psychometric properties of the RUDAS compared with a reference standard for diagnosing dementia or compared the RUDAS to other cognitive assessment tools. Two independent reviewers screened the abstracts and full-text articles and reviewed bibliographies for further references. We extracted data using standardized forms and assessed studies for risk of bias. RESULTS: Our search resulted in 148 articles, from which 11 were included. The RUDAS was assessed in 1236 participants and was found to have a pooled sensitivity of 77.2% (95% confidence interval [CI] 67.4-84.5) and a pooled specificity of 85.9% (95% CI 74.8-92.6) yielding a positive likelihood ratio of 5.5 (95% CI 2.9-10.7) and a negative likelihood ratio of 0.27 (95% CI 0.17-0.40). A pooled estimate of the correlation between the RUDAS and the Mini-Mental State Examination (MMSE) was 0.77 (95% CI 0.72-0.81). Results of the RUDAS were less affected by language and education level than the MMSE. INTERPRETATION: The RUDAS is a brief and freely available cognitive assessment tool with reasonable psychometric characteristics that may be particularly useful in patients with diverse backgrounds.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".