Performance of three cognitive screening tools in a sample of older New Zealanders
Bibliographic record
Abstract
BACKGROUND: With the ubiquitous Mini-Mental State Exam now under copyright, attention is turning to alternative cognitive screening tests. The aim of the present study was to investigate three common cognitive screening tools: the Montreal Cognitive Assessment (MoCA), the Rowland Universal Dementia Assessment Scale (RUDAS), and the recently revised Addenbrooke's Cognitive Assessment Version III (ACE-III). METHODS: The ACE-III, MoCA and RUDAS were administered in random order to a sample of 37 participants with diagnosed mild dementia and 47 comparison participants without dementia. The diagnostic accuracy of the three tests was assessed. RESULTS: All the tests showed good overall accuracy as assessed by area under the ROC Curve, 0.89 (95% CI = 0.80-0.95) for the ACE-III, 0.84 (0.75-0.91) for the MoCA, and 0.86 (0.77-0.93) for RUDAS. The three tests were strongly correlated: r(84) = 0.85 (0.78-0.90) between the ACE-III and MoCA, 0.70 (0.57-0.80) between the ACE-III and RUDAS; and 0.65 (0.50-0.76) between the MoCA and RUDAS. The data derived optimal cut-off points for were lower than the published recommendations for the ACE-III (optimal cut-point ≤76, sensitivity = 81.1%, specificity = 85.1%) and the MoCA (≤20, sensitivity = 78.4%, specificity = 83.0%), but similar for the RUDAS (≤22, sensitivity = 78.4%, specificity = 85.1%). CONCLUSIONS: All three tools discriminated well overall between cases of mild dementia and controls. To inform interpretation of these tests in clinical settings, it would be useful for future research to address more inclusive and potentially age-stratified local norms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".