MONTREAL COGNITIVE ASSESSMENT VS. ROWLAND UNIVERSAL DEMENTIA ASSESSMENT SCALE FOR COGNITIVE SCREENING
Bibliographic record
Abstract
Our study involved 208 consecutive patients seen in an outpatient memory clinic in London, Ontario, Canada (63 with diagnosis of mild dementia, 86 with diagnosis of mild cognitive impairment, 59 with normal cognition) for whom both a MoCA (Montreal Cognitive Assessment) and RUDAS (Rowland Universal Dementia Assessment Scale) could be completed. The sensitivity and specificity of both measures were assessed for detection of mild cognitive impairment and dementia. Using a cutoff score of 25 or less for both, the MoCA had a sensitivity of 97 % to detect dementia, with only 31% specificity, while the RUDAS had a 94% sensitivity to detect dementia, with 54% specificity. The MoCA at 25 or less had a sensitivity of 95% and a specificity of 69% to detect mild cognitive impairment, while the RUDAS at 25 had a sensitivity of 81% and a specificity of 88% to detect mild cognitive impairment. RUDAS score variation with educational attainment is significantly smaller than MoCA score variation (P<0.01). The RUDAS is significantly briefer than the MoCA as a cognitive screening tool, and demonstrated similar sensitivity for dementia, with much better specificity for dementia and mild cognitive impairment, in an outpatient memory clinic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".