Behavioral Quantitation Is More Sensitive Than Cognitive Testing in Frontotemporal Dementia
Bibliographic record
Abstract
OBJECTIVE: To compare behavioral and cognitive testing in the clinical diagnosis of frontotemporal dementia (FTD). METHODS: A clinically defined cohort of FTD (n = 52) is compared with 52 Alzheimer disease (AD) patients on a Frontal Behavioral Inventory (FBI) and cognitive tests (e.g., Mini-Mental State Examination, Mattis Dementia Rating Scale, Western Aphasia Battery, Wechsler Intelligence Scale, Wechsler Memory Scale). Fourteen patients with FTD had autopsy confirmation, and their tests are also compared with the rest of the FTD population. RESULTS: The FTD and AD groups were matched in sex, duration, and severity of dementia. The total scores on the FBI showed the largest difference. Mini-Mental State Examination and Mattis Dementia Rating Scale total scores did not discriminate between the two groups. Memory subscores were lower in the AD group, and conceptualization and language-related scores were worse in the FTD group. Milder and earlier affected patients, who could carry on a large battery of neuropsychological tests, were much better distinguished by the FBI scores on discriminant function analysis. In contrast to 78% by the cognitive tests, 98% of the FTD and AD patients were differentiated by the FBI. CONCLUSIONS: Although memory scores were lower in AD and language scores in the FTD population, many of the cognitive tests do not distinguish between FTD and AD. On the other hand, a behavioral inventory is a useful adjunct in the diagnosis of FTD. Postmortem validation was carried out in a sizeable subset of the population, showing similar behavioral and cognitive data.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".