Montreal Cognitive Assessment (MoCA) Performance and Domain-Specific Index Scores in Amnestic <i>Versus</i> Aphasic Dementia
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is a popular and simple-to-administer screening instrument to detect cognitive impairment. The MoCA generates a total score and six domain-specific index scores: (1) Memory, (2) Executive Functioning, (3) Attention, (4) Language, (5) Visuospatial, and (6) Orientation. It is unclear whether these MoCA scores can differentiate between distinct clinical dementia syndromes. This study compared MoCA Index scores between amnestic dementia of the Alzheimer's type (DAT) and primary progressive aphasia (PPA), a language-based dementia. METHOD: Baseline MoCA data were analyzed from 33 DAT, 37 PPA, and 83 cognitively normal individuals enrolled in the Clinical Core of the Northwestern Alzheimer's Disease Center. A one-way analysis of covariance adjusted for age was used to compare MoCA scores among groups. A logistic regression model was implemented to observe individual likelihood of group affiliation based on MoCA Index scores. RESULTS: The mean MoCA total score was significantly higher in controls compared to both patient groups (p < .001) but did not differ between DAT and PPA groups. However, in accordance with salient clinical features commonly observed in DAT versus PPA, Memory and Orientation Index scores were lowest in the DAT group (p < .001), whereas Language and Attention Index scores were lowest in the PPA group (p < .001). Multivariate logistic regression analysis showed that the individual effects of Memory (p = .001), Language (p = .002), and Orientation (p = .025) Indices were significant. CONCLUSIONS: MoCA Index scores can help differentiate among distinct cognitive syndromes, suggesting it may be a useful brief screening tool to detect domain-specific cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".