Measuring verb argument structure in nonfluent primary progressive aphasia: a one-year case study
Bibliographic record
Abstract
Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by progressive language decline, with nonfluent variant PPA (nfvPPA) typically affecting speech production and syntactic processing. Verb usage is particularly vulnerable, given its role in sentence construction and grammatical encoding. To examine longitudinal changes in verb production and argument structure in a patient with nfvPPA, using narrative discourse analysis and performance in a specific task, over a one-year interval. This case study forms part of a broader investigation involving neurological and language assessments in PPA. Narrative samples were elicited using the PICNIC scene from the Montreal-Toulouse Language Assessment Battery (MLT), and syntactic abilities were evaluated through the Argument Structure subtest of the Northwestern Assessment of Verbs and Sentences (NAVS). Data from 2023 and 2024 were compared. There was a 71.4% reduction in total verb usage in the narrative task, with intransitive verbs decreasing by 100% and transitive verbs by 60%. Greater hesitation and simplification of syntactic structures were observed, including substitution of complex verbs (e.g., "tentar") with simpler gerund forms. NAVS results showed reduced accuracy in producing sentences with two or three obligatory arguments, declining by 40% and 100%, respectively. Findings reflect progressive syntactic degradation in nfvPPA. Verb argument structure analysis proved sensitive to subtle grammatical deficits, revealing impairments not captured by traditional tasks. This aligns with literature linking dorsal language tract degeneration to syntactic breakdown. Argument structure analysis is a valuable tool for assessing and monitoring syntactic decline in nfvPPA, with clinical implications for diagnosis and intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".