A Spanish Neuropsychological Battery Discriminates Between the Behavioral Variant of Frontotemporal Dementia and Primary Progressive Aphasia in a Colombian Sample
Bibliographic record
Abstract
The differential diagnosis among the behavioral variant of frontotemporal dementia FTD (bvFTD) and the linguist one primary progressive aphasia (PPA) is challenging. Presentations of dementia type or variants dominated by personality change or aphasia are frequently misinterpreted as psychiatric illness, stroke, or other conditions. Therefore, it is important to identify cognitive tests that can distinguish the distinct FTD variants to reduce misdiagnosis and best tailor interventions. We aim to examine the discriminative capacity of the most frequently used cognitive tests in their Spanish version for the context of dementia evaluation as well as the qualitative aspects of the neuropsychological performance such as the frequency and type of errors, perseverations, and false positives that can best discriminate between bvFTD and PPA. We also described mood and behavioral profiles of participants with mild to moderate probable bvFTD and PPA. A total of 55 subjects were included in this cross-sectional study: 20 with PPA and 35 with bvFTD. All participants underwent standard dementia screening that included a medical history and physical examination, brain MRI, a semistructured caregiver interview, and neuropsychological testing. We found that bvFTD patients had worse performance in executive function tests, and the PPA presented with the lower performance in language tests and the global score of Mini-Mental State Examination (MMSE). After running the linear discriminant model, we found three functions of cognitive test and subtests combination and three functions made by the Montreal Cognitive Assessment (MoCA) language subtest and performance errors that predicted group belonging. Those functions were more capable to classify bvFTD cases rather than PPA. In conclusion, our study supports that the combination of an individual test of executive function and language, MoCA's subtest, and performance errors as well have good accuracy to discriminate between bvFTD and PPA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".