Platelets
Bibliographic record
Abstract
Aortic stenosis (AS) is the most common heart valve disease requiring surgery in developed countries, with a rising global burden associated with aging populations. The predominant cause of AS is believed to be driven by calcific degeneration of the aortic valve and a growing body of evidence suggests that platelets play a major role in this disease pathophysiology. Furthermore, platelets are a player in bioprosthetic valve dysfunction caused by their role in leaflet thrombosis and thickening. This review presents the molecular function of platelets in the context of recent and rapidly evolving understanding the role of platelets in AS, both of the native aortic valve and bioprosthetic valves, where there remain concerns about the effects of subclinical leaflet thrombosis on long-term prosthesis durability. This review also presents the role of antiplatelet and anticoagulation therapies on modulating the impact of platelets on native and bioprosthetic aortic valves, highlighting the need for further studies to determine whether these therapies are protective and may increase the life span of surgical and transcatheter aortic valve implants. By linking molecular mechanisms through which platelets drive disease of native and bioprosthetic aortic valves with studies evaluating the clinical impact of antiplatelet and antithrombotic therapies, we aim to bridge the gaps between our basic science understanding of platelet biology and their role in patients with AS and ensuing preventive and therapeutic implications.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".