Can RUDAS Be an Alternate Test for Detecting Mild Cognitive Impairment in Older Adults, Thailand?
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is the commonly used cognitive test for detecting mild cognitive impairment (MCI) in Thailand. Nevertheless, cultural biases and educational levels influence its performance. The Rowland Universal Dementia Assessment Scale (RUDAS) seems to lower the limitation of the MoCA. This study aimed to compare the performance of the RUDAS and the MoCA for the diagnosis of MCI and demonstrate the correlation between them. A cross-sectional study of 150 older participants from the outpatient setting of the Internal Medicine Department, Srinagarind Hospital, Thailand was recruited during January 2020 and March 2021. The diagnostic properties in detecting MCI of the RUDAS and the MoCA were compared. MCI was diagnosed in 42 cases (28%). The AUC for both RUDAS (0.82, 95% CI 0.75-0.89) and MoCA (0.80, 95% CI 0.72-0.88) were similar. A score of 25/30 provided the best cut-off point for the RUDAS (sensitivity 76.2%, specificity 75%) and a score of 19/30 for the MoCA had sensitivity and specificity of 76.2% and 71.3%. The Spearman's correlation coefficient between both tests was 0.6. In conclusion, the RUDAS-Thai could be an option for MCI screening. It was correlated moderately to the MoCA.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".