Lack of Frank Agrammatism in the Nonfluent Agrammatic Variant of Primary Progressive Aphasia
Bibliographic record
Abstract
BACKGROUND/AIMS: Frank agrammatism, defined as the omission and/or substitution of grammatical morphemes with associated grammatical errors, is variably reported in patients with nonfluent variant primary progressive aphasia (nfPPA). This study addressed whether frank agrammatism is typical in agrammatic nfPPA patients when this feature is not required for diagnosis. METHOD: We assessed grammatical production in 9 patients who satisfied current diagnostic criteria. Although the focus was agrammatism, motor speech skills were also evaluated to determine whether dysfluency arose primarily from apraxia of speech (AOS), instead of, or in addition to, agrammatism. Volumetric MRI analyses provided impartial imaging-supported diagnosis. RESULTS: The majority of cases exhibited neither frank agrammatism nor AOS. CONCLUSION: There are nfPPA patients with imaging-supported diagnosis and preserved motor speech skills who do not exhibit frank agrammatism, and this may persist beyond the earliest stages of the illness. Because absence of frank agrammatism is a subsidiary diagnostic feature in the logopenic variant of PPA, this result has implications for differentiation of the nonfluent and logopenic variants, and indicates that PPA patients with nonfluent speech in the absence of frank agrammatism or AOS do not necessarily have the logopenic variant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".