Investigating Noun and Verb Naming in Behavioral Variant of Frontotemporal Dementia and Non-Patients Persian-Speaking
Bibliographic record
Abstract
Introduction: Due to the prevalence of cognitive disorders, such as the behavioral variant of frontotemporal dementia (bvFTD) and the consequences that these disorders follow, early diagnosis and awareness of the deficiencies of these people in the cognitive and language areas is essential. Given that language is dependent on culture, examining the linguistic characteristics of such patients in different languages can provide valuable findings. Therefore, this study compares noun and verb naming abilities in individuals with bvFTD and non-patients Persian-speaking. Materials and Methods: In this cross-sectional study, 3 cognitive tests, including frontal assessment battery (FAB), Montreal cognitive assessment, and mini-mental state examination (MMSE), along with 2 noun naming and verb naming tests were performed on 15 patients with bvFTD and 30 homogeneous non-patient individuals. Results: The bvFTD group had significantly different scores for both noun and verb naming compared to the non-patient group (P<0.05). Meanwhile, the bvFTD group was more impaired in naming verbs than nouns, with the largest difference between groups in the verb naming task. Conclusion: the results showed that bvFTD patients have poorer noun and verb naming abilities than non-patients. In particular, in verb naming, they showed more deficits than nouns. One possible explanation is that the processing of verbs is more complicated than nouns and involves a more complex neural system and cognitive processes than noun processing. Another possibility is that verbs rely more heavily on frontal and temporal regions of the brain, which are typically affected by bvFTD.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".