Neurological, cognitive and psychiatric features of Primary Progressive Aphasia: a naturalistic study of a large cohort over 10 years
Bibliographic record
Abstract
BACKGROUND: Patients with Primary Progressive Aphasias (PPAs) almost systematically inquire about the longitudinal evolution of their disease in clinics but very little research exists on the issue. METHOD: We studied 82 PPA patients from the Research Chair on PPA - Fondation de la Famille Lemaire Cohort over a 10-year span (42 logopenic, 21 non-fluent/agrammatic and 19 semantic PPAs) and collected data from 5 domains (language, cognition, motor, psychiatric, functional) at 5 time points from onset to death. Logistical regression analyses and repeated measures ANOVAs were conducted to delineate the longitudinal profile of each variant PPA. RESULT: All patients presented anomia and executive impairments over time. Language deficits tended to be more significant for lvPPA, particularly after 3 years of evolution and this group showed broader cognitive impairments. Psychiatric symptoms were more frequent in svPPA and nfvPPA, particularly after 5 years of evolution. Motor features predominantly affected patients with nfvPPA after 2 years of evolution. Overall functional abilities remained preserved the longest in svPPA (up to 5 years). CONCLUSION: To our knowledge, this naturalistic study on all major PPA symptoms over a 10-year span from onset to death is the largest to date. Data from this study can help clinicians better inform and prepare their patients for future challenges as well as design more focused interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".