Treating Alzheimer's Disease With Cholinesterase Inhibitors: What Have We Learned So Far?
Bibliographic record
Abstract
For almost 90 years following the original description of Alois Alzheimer's patient and the identification of Alzheimer's disease (AD) (Alzheimer, 1907), physicians faced the bleak prospect of observing the inexorable and relentless decline in cognition, function, and behavior with little or no opportunity for therapeutic intervention. In the last 5 years clinicians have finally been provided with a class of medications, the cholinesterase (ChE) inhibitors, which have passed the test of efficacy and safety in the symptomatic management of AD and related dementias. With the arrival of donepezil, rivastigmine, and galantamine as the second generation of ChE inhibitors, a renewed and sustained interest in the diagnosis and care of AD patients might have been anticipated. However, there remains residual therapeutic nihilism and skepticism over the utility of these treatments in some quarters of the medical community and among some paying authorities. In moving forward and addressing these concerns, we must reflect carefully on the question, “What have we learned about the ChE inhibitors so far?”
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".