MMSE Scores Decline at a Greater Rate in Frontotemporal Degeneration than in AD
Bibliographic record
Abstract
The clinical diagnostic criteria for frontotemporal degeneration (FTD) include relative preservation of memory and visuospatial function, in contradistinction to characteristics of Alzheimer's disease (AD). The Mini-Mental State Examination (MMSE) contains items to assess these areas of cognition. In a retrospective case-control study of participants at two institutionally-based AD centers, we determined whether total MMSE and MMSE subscores would reflect the disease progression projected by the clinical criteria of FTD vs. AD. Participants were 44 subjects with FTD (7 pathologically confirmed) and 45 with pathologically confirmed AD. Each subject had at least two MMSEs with minimum inter-test intervals of 9 months. We compared annualized rates of change for total MMSE scores and cognitive domain subscores over time and between groups by two independent samples t-tests and proportion tests. The total MMSE score (p = 0.03) and language subscore (p = 0.02) showed a greater rate of decline for the FTD group than the AD group, although the constructional praxis item declined less rapidly in the FTD group (p = 0.018). Changes in MMSE subscores paralleled the clinical diagnostic criteria for FTD. The more rapid progression on the language subscore was observed in both language and behavioral variants of FTD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".