The impact of cholinesterase inhibitors on cognitive trajectories in mild cognitive impairment patients based on amyloid beta status
Bibliographic record
Abstract
INTRODUCTION: This study examines whether cholinesterase inhibitors (ChEIs) influence the progression to Alzheimer's disease (AD) dementia and cognitive trajectories in amnestic mild cognitive impairment (MCI) patients, considering their amyloid beta (Aβ) status. METHODS: Kaplan-Meier and time-varying Cox models evaluated ChEI use and different Aβ status on MCI-to-AD progression. Linear mixed-effects models assessed cognitive trajectories. Locally estimated scatterplot smoothing regression analyzed cognitive changes before and after ChEI initiation. RESULTS: Among 558 amnestic MCI participants (168 ChEI users), ChEI users exhibited higher risk of progression to AD dementia (hazard ratio = 1.77, 95% confidence interval: 1.15 to 2.73, p = 0.001). Both ChEI use and Aβ burden independently accelerated MCI progression and cognitive decline. Cognitive trajectories demonstrated decline before ChEI initiation and continued to decline after treatment began. DISCUSSION: The association between ChEI treatment and accelerated progression to AD dementia and cognitive decline, independent of Aβ status, emphasized the need to reconsider optimal timing for ChEI initiation in MCI. HIGHLIGHTS: ChEI use in MCI was associated with increased risk of progression to AD dementia. ChEI use in MCI was associated with accelerated longitudinal cognitive decline. Cognitive decline persisted after ChEI initiation rather than reversing. ChEI effects on MCI progression to AD dementia were independent of Aβ status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".