Optimal use of cholinergic drugs in Alzheimer’s disease
Bibliographic record
Abstract
Sir We read with great interest the review article by Hampel et al. (2018) on the cholinergic system in Alzheimer’s disease. Sadly, despite the strength of the evidence towards a beneficial effect of cholinesterase inhibitors in Alzheimer’s disease, the government of France withdrew reimbursement for these medications. While we ultimately hope for a precision medicine approach to treatment as suggested by Hampel et al., there is thus still an immediate need to better define the efficacy of symptomatic drugs in Alzheimer’s disease. In fact our group demonstrated the feasibility to quantify presynaptic cholinergic depletion in vivo (Aghourian et al., 2017). We propose an approach used in the treatment of major depression, where ‘treatment-resistant depression’ leads to change in treatment, such as a medication switch within a class or between class, and augmentation using various pharmacological and non-pharmacological means (McIntyre et al., 2014). An operational definition of ‘cholinesterase inhibitors non-responsive dementia’ could be agreed upon, and instead of indefinite prescription of cholinesterase inhibitors if well tolerated, as is currently done in most countries, a more structured approach to assessment of benefit would lead to cholinesterase inhibitor cessation or treatment modification based on the most prominent symptoms at the current stage of Alzheimer’s disease for individual patients. Along these lines, we are encouraged that evidence-based recommendations for deprescribing cholinesterase inhibitors are already appearing (Reeve et al., 2018). We think that this more structured approach to therapy in Alzheimer’s disease will encourage clinicians to use cholinesterase inhibitors when appropriate, and reassure third party payers that these drugs are used optimally. This will be even more important for new generations of drugs acting on various components of Alzheimer’s disease pathophysiology. Data sharing is not applicable to this article as no new data were created or analysed in this study. The authors report no competing interests.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".