Are we over estimating cognitive impairment in patients who have a Primary Progressive Aphasia Diagnosis - Is the MOCA appropriate?
Bibliographic record
Abstract
Abstract Background Cognitive ability has been shown to be associated with functional ability1. Primary Progressive Aphasia (PPA) is a subtype of dementia characterised by early language impairment, which may not fully reflect broader cognitive or functional decline.2 The Montreal Cognitive Assessment (MOCA), widely used in dementia assessment, is language-heavy and may underestimate functional ability in individuals with PPA. Methods We retrospectively analysed data from 26 patients attending our Integrated Care clinic. Six were diagnosed with PPA; the remaining 20 had Alzheimer’s disease, Lewy Body Dementia, Vascular Dementia, or Mild Cognitive Impairment (MCI). Standardised measures included the MOCA, Barthel Index (basic activities of daily living), Lawton-Brody scale (instrumental activities of daily living), and Clinical Frailty Scale (CFS). We used Mann-Whitney U tests to compare cognitive and functional scores between groups and Spearman correlations to explore associations between MOCA and functional measures. Results PPA patients scored lower on the MOCA (median 13.5) compared to non-PPA patients (median 19), though not statistically significant (U = 24.5, p = 0.118). Despite this, Barthel and Lawton-Brody scores remained high in the PPA group, with a trend toward higher Lawton scores (U = 96.5, p = 0.084). Spearman correlation analysis showed no significant correlation between MOCA scores and functional measures in either group. Conclusion Patients with PPA may demonstrate significant language-related cognitive impairment without corresponding declines in functional independence. Our findings suggest that reliance on MOCA alone may potentially underestimate cognitive abilities in PPA, highlighting the need for multimodal assessment in this population.
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 | 0.023 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".