Current Pharmacological and Emerging Non-pharmacological Treatments in Slowing the Progression of Mild Cognitive Impairment: A Literature Review
Bibliographic record
Abstract
Introduction: MCI is considered as a prodromal stage between normal cognitive aging and dementia given its potential to develop into various forms of dementia, most notably Alzheimer’s disease (AD). This translates to a need for effective pharmacological and non-pharmacological treatments to prevent the progression of MCI and subsequently slowing AD onset. This review aims to discuss the effectiveness of pharmacological and non-pharmacological interventions in slowing MCI progression. Methods: A literature search was conducted using the PubMed database for randomized controlled trials (RCTs) examining the effectiveness of interventions with individuals with MCI. Keywords included “mild cognitive impairment”, “drug”, “treatment”, and “randomized controlled trials”. Articles were evaluated on criteria relevant to the review’s purpose. Results: Studies on different pharmacological and non-pharmacological interventions demonstrated promising results in slowing the progression of MCI into dementia. Acetylcholinesterase inhibitors (AChEIs) display favourable results on multiple cognitive assessments when compared to placebo. Non-pharmacological interventions, such as diet supplementation or exercise, also have the potential in improving performance in a multitude of cognitive domains. Discussion: In multiple RCTs, AChEIs displayed effectiveness in alleviating cognitive impairment associated with MCI, but only temporarily with some adverse effects. Given the difficulty in determining a clear use of AChEIs on slowing the progression of MCI, additional research is needed. Non-pharmacological interventions have also displayed effectiveness without risk of adverse drug effects. Literature regarding multimodal approaches combining both pharmacological and non-pharmacological interventions is a novel area of research, and these studies have suggested positive additive effects. Conclusion: Pharmacological and non-pharmacological interventions for slowing the progression of MCI display promising results. More studies are needed to determine which treatment plans, whether pharmacological, non-pharmacological, or a combination of the two, will prove to be the most effective for individuals with MCI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".