A Systematic Review of Clinical Practice Guidelines for Alzheimer’s Disease and Strategies for Future Advancements
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a disease continuum from pathophysiologic, biomarker and clinical perspectives. With the advent of advanced technologies, diagnosing and managing patients is evolving. METHODS: A systematic literature review (SLR) of practice guidelines for mild cognitive impairment (MCI) and AD dementia was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This systematic literature review (SLR) aimed to summarize current clinical practice guidelines for screening, testing, diagnosis, treatment and monitoring in the AD continuum. The results of this SLR were used to propose a way forward for practice guidelines given the possible introduction of biomarker-guided technology using blood- or plasma-based assays and disease-modifying treatments (DMTs) targeted for early disease. RESULTS: 53 clinical practice guidelines were identified, 15 of which were published since 2018. Screening for asymptomatic populations was not recommended. Biomarker testing was not included in routine diagnostic practice. There was no consensus on which neurocognitive tests to use to diagnose and monitor MCI or AD dementia. Pharmacologic therapies were not recommended for MCI, while cholinesterase inhibitors and memantine were recommended for AD treatment. DISCUSSION: The pre-2018 and post-2018 practice guidelines share similar recommendations for screening, diagnosis and treatment. However, once DMTs are approved, clinicians will require guidance on the appropriate use of DMTs in a clinical setting. This guidance should include strategies for identifying eligible patients and evaluating the DMT benefit-to-risk profile to facilitate shared decision-making among physicians, patients and care partners. CONCLUSION: Regular evidence-based updates of existing guidelines for the AD continuum are required over the coming decades to integrate rapidly evolving technologic and medical scientific advances and bring emerging approaches for management of early disease into clinical practice. This will pave the way toward biomarker-guided identification and targeted treatment and the realization of precision medicine for AD.
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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 | 0.068 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".