Are we comparing frontotemporal dementia and Alzheimer disease patients with the right measures?
Bibliographic record
Abstract
BACKGROUND: Clinical research studies of behavioral variant frontotemporal dementia (bvFTD) often use Alzheimer disease (AD) as a comparison group for control of dementia variables, using tests of cognitive function to match the groups. These two dementia syndromes, however, are very different in clinical manifestations, and the comparable severity of these dementias may not be reflected by commonly used cognitive scales such as the Mini-Mental State Examination (MMSE). METHODS: We evaluated different measures of dementia severity and symptoms among 20 people with bvFTD compared to 24 with early-onset AD. RESULTS: Despite similar ages, disease-duration, education, and cognitive performance on two tests of cognitive function, the MMSE and the Montreal Cognitive Assessment (MoCA), the bvFTD participants, compared to the AD participants, were significantly more impaired on other measures of disease severity, including function (Functional Assessment Questionnaire (FAQ)), neuropsychiatric symptoms (Neuropsychiatric Inventory (NPI)), and global dementia stage (Clinical Dementia Rating Scales (CDRs)). However, when we adjusted for the frontotemporal lobar degeneration-CDR (FTLD-CDR) in the analyses, the two dementia groups were comparable across all measures despite significant differences on the cognitive scales. CONCLUSION: We found tests of cognitive functions (MMSE and MoCA) to be insufficient measures for ensuring comparability between bvFTD and AD groups. In clinical studies, the FTLD-CDR, which includes additional language and behavior items, may be a better overall way to match bvFTD and AD groups on dementia severity.
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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 | 0.087 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".