Study on the effectiveness of acetylcholinesterase inhibitors in the treatment of primary progressive logopenic aphasia
Bibliographic record
Abstract
Abstract Background For over 25 years, cholinesterase inhibitors (ChEIs) have been the main symptomatic treatment for Alzheimer’s disease (AD). Several meta‐analyses have supported their effectiveness in various neurocognitive aspects of amnestic AD. Over 86% of cases of the logopenic variant of primary progressive aphasia (lvPPA) are caused by a similar pathologic process than AD, yet no study has examined the efficacy of ChEIs in this AD variant. We aimed to explore the efficacy of ChEIs in the treatment of lvPPA by comparing their longitudinal MMSEs to that of treated amnestic AD patients. Method A retrospective chart review was performed on 48 patients with lvPPA and 52 patients with amnestic AD. Both groups were similar with respect to age, level of education, and onset of symptoms. Drug history and MMSE scores were collected on several time points before and after the introduction of ChEIs. Results Patients with lvPPA showed a similar trajectory of decline than amnestic AD patients on serial MMSEs (both WORLD and 100‐7) up to twelve months after the introduction of ChEIs (Figure 1 and 2). Conclusion This study provides preliminary evidence for efficacy of ChEIs in patients with lvPPA and suggests similar benefits to those seen in amnestic AD patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".