Bibliographic record
Abstract
BACKGROUND: Heart transplantation has become the standard of care for pediatric patients with end-stage heart disease, and outcomes have consistently improved over the last few decades. CAV, however, remains a leading cause of morbidity and mortality in heart transplantation and is the leading cause of death beyond 3 years post-transplantation. We sought out to provide an in-depth overview of CAV in the pediatric heart transplant population. METHODS: Database searches were conducted in both Medline and Embase on the topic of cardiac vasculopathy in pediatric heart transplant recipients. The search used five broad concept terms: heart transplant; pediatric; CAV; diagnosis, prognosis, and risk factors; and guidelines and reviews. References were captured if there was at least one term in each of the concepts. The search was limited to articles in the English language. RESULTS: A total of 148 articles were identified via the literature search with further articles identified via review of references. Pediatric data regarding the etiology and development of CAV remain limited although knowledge about the immune and non-immune factors playing a role are increasing. CAV continues to be difficult to detect with many invasive and non-invasive methods available, yet their effectiveness in the detection of CAV remains suboptimal. There remains no proven medical intervention to treat or reverse established CAV disease, and CAV is associated with high rates of graft loss once detected. However, several medications are used in hopes of preventing, slowing progression, or modifying the outcomes. CONCLUSION: This review provides a comprehensive overview of CAV, discusses its clinical presentation, risk factors, diagnostic tools used to identify CAV in the pediatric population, and highlights the current therapeutic options and the need for ongoing research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".