Advances in the Diagnosis and Management of Cardiac Amyloidosis: A Literature Review
Bibliographic record
Abstract
Cardiac amyloidosis, increasingly recognized for its significant impact on global heart health and patient survival, demands a thorough review to understand its complexity and the urgency of improved management strategies. As a cause of cardiomyopathy and heart failure, particularly in patients with aortic stenosis and atrial fibrillation, this condition also relates to higher incidences of dementia in the affected populations. The objective of this review was to integrate and discuss the latest advancements in diagnostics and therapeutics for cardiac amyloidosis, emphasizing the implications for patient prognosis. We evaluated the latest literature from major medical databases such as PubMed and Scopus, focusing on research from 2020 to 2024, to gather comprehensive insights into the current landscape of this condition. Insights from our review highlight the complex pathophysiology of cardiac amyloidosis and the diagnostic challenges it presents. We detail the effectiveness of emerging treatments, notably gene silencing therapies like patisiran and vutrisiran, which offer transformative potential by targeting the production of amyloidogenic proteins. Additionally, the stabilization therapy acoramidis shows promise in modifying disease progression and improving clinical outcomes. This review underscores the critical need for updated clinical guidelines and further research to expand access to groundbreaking therapies and enhance disease management. Advocating for continued research and policy support, we emphasize the importance of advancing diagnostic precision and treatment effectiveness, which are vital for improving patient outcomes and addressing this debilitating disease globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".