Cardiac Valvular Pathology: Comparative Pathology and Animal Models of Acquired Cardiac Valvular Diseases
Bibliographic record
Abstract
Recent voluntary withdrawal of the ergoline-derivative Alzheimers' drug Pergolide (Permax) resulting from demonstrated risk of cardiac valve injury illustrates the increased importance of valve injury in pharmaceutical toxicology. Following the 2001 landmark discovery of cardiac valve injury associated with the widely prescribed anti-obesity drug combination fenfluramine-phentermine, and subsequent withdrawal, the need to understand and assess cardiac valve biology and pathology both preclinically and clinically has been accentuated. Unique aspects of the developmental biology, anatomy, and physiology of cardiac valves compared to main cardiac tissue have been discovered, and key elements of the pathophysiology of various valvular injury mechanisms have been described. Although general clinical cardiac valvular disease in humans has been well characterized, animal modeling of valvular injury has proved to be difficult and undersubscribed. Additionally, both the preclinical, pharmaceutical, toxicologic assessment of valvular injury and the understanding of species-comparative valvular pathology have been limited. As discoveries and awareness grows, the purpose of this paper is to review the structure and function of cardiac valves, mechanisms, and outcomes of the common acquired human cardiac valve diseases, including those that are drug-related; to summarize comparative laboratory animal valvular pathology; and to review the literature of contemporary animal models of valvular injury.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".