Nonfluent/Agrammatic PPA with In-Vivo Cortical Amyloidosis and Pick’s Disease Pathology
Bibliographic record
Abstract
The role of biomarkers in predicting pathological findings in the frontotemporal dementia (FTD) clinical spectrum disorders is still being explored. We present comprehensive, prospective longitudinal data for a 66 year old, right-handed female who met current criteria for the nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA). She first presented with a 3-year history of progressive speech and language impairment mainly characterized by severe apraxia of speech. Neuropsychological and general motor functions remained relatively spared throughout the clinical course. Voxel-based morphometry (VBM) showed selective cortical atrophy of the left posterior inferior frontal gyrus (IFG) and underlying insula that worsened over time, extending along the left premotor strip. Five years after her first evaluation, she developed mild memory impairment and underwent PET-FDG and PiB scans that showed left frontal hypometabolism and cortical amyloidosis. Three years later (11 years from first symptom), post-mortem histopathological evaluation revealed Pick's disease, with severe degeneration of left IFG, mid-insula, and precentral gyrus. Alzheimer’s disease (AD) (CERAD frequent/Braak Stage V) was also detected. This patient demonstrates that biomarkers indicating brain amyloidosis should not be considered conclusive evidence that AD pathology accounts for a typical FTD clinical/anatomical syndrome.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".