The role of acetylcholinesterase inhibitors such as neostigmine and rivastigmine on chronic pain and cognitive function in aging: A review of recent clinical applications
Bibliographic record
Abstract
Chronic pain in patients with Alzheimer's disease or dementia is a complex issue in the medical field; these patients suffer from the common causes of chronic pain, especially in geriatric medicine. To ensure the correct type and level of given treatment, medical care should be taken to avoid the contribution of chronic pain and cognitive impairment in the elderly population. Acetylcholinesterase inhibitors (AChE-Is) have been proven as an efficient therapeutic resource for significant improvement in dementia of Alzheimer's disease and chronic pain due to the fact that cholinergic deficit is considered as an early finding in cognitive impairment and persisting pain. Some AChE-Is are investigated here in terms of treatment of dementia and chronic pain management. Neostigmine has been used as an adjunct analgesic in the postoperative period and in combination with other analgesic medications in an intrathecal approach. Rivastigmine has, over the past ten years, become the approved agent for the management of dementia of mild to moderate Alzheimer's disease and has gained approval for treating different types of non-Alzheimer's dementia. In this review, we will focus on the two types of AChE-Is (rivastigmine and neostigmine) in the development of their clinical use and their respective mechanisms of actions on improving cognitive function and managing chronic pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".