Detection and Differentiation of Frontotemporal Dementia and Related Disorders From Alzheimer Disease Using the Montreal Cognitive Assessment
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a cognitive screening tool used by practitioners worldwide. The efficacy of the MoCA for screening frontotemporal dementia (FTD) and related disorders is unknown. The objectives were: (1) to determine whether the MoCA detects cognitive impairment (CI) in FTD subjects; (2) to determine whether Alzheimer disease (AD) and FTD subtypes and related disorders can be parsed using the MoCA; and (3) describe longitudinal MoCA performance by subtype. We extracted demographic and testing data from a database of patients referred to a cognitive neurology clinic who met criteria for probable AD or FTD (N=192). Logistic regression was used to determine whether dementia subtypes were associated with overall scores, subscores, or combinations of subscores on the MoCA. Initial MoCA results demonstrated CI in the majority of FTD subjects (87%). FTD subjects (N=94) performed better than AD subjects (N=98) on the MoCA (mean scores: 18.1 vs. 16.3; P=0.02). Subscores parsed many, but not all subtypes. FTD subjects had a larger decline on the MoCA within 13 to 36 months than AD subjects (P=0.02). The results indicate that the MoCA is a useful tool to identify and track progression of CI in FTD. Further, the data informs future research on scoring models for the MoCA to enhance cognitive screening and detection of FTD patients.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 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".