Stenosi valvolare aortica degenerativa: alla ricerca di una prevenzione farmacologica
Bibliographic record
Abstract
Degenerative calcific aortic valve stenosis (CAVS) is a chronic disease whose prevalence has increased over the last decade because of the aging of the general population. CAVS pathogenesis is characterized by complex molecular and cellular mechanisms that promote valve fibro-calcific remodeling. During the first phase, referred to as initiation, the valve undergoes collagen deposition and lipid and immune cell infiltration due to mechanical stress. Subsequently, during the progression phase, the aortic valve undergoes chronic remodeling through osteogenic and myofibroblastic differentiation of interstitial cells and matrix calcification. Knowledge of the mechanisms underlying CAVS development supports the resort to potential therapeutic strategies that interfere with fibro-calcific progression. Currently, no medical therapy has demonstrated the ability to significantly prevent CAVS development or slow its progression. The only treatment available in symptomatic severe stenosis is surgical or percutaneous aortic valve replacement. The aim of this review is to highlight the pathophysiological mechanisms involved in CAVS pathogenesis and progression and to discuss potential pharmacological treatments able to inhibit the main pathophysiological mechanisms of CAVS, including lipid-lowering treatment with lipoprotein(a) as emergent therapeutic target.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".