Rejection surveillance in pediatric heart transplant recipients: Critical reflection on the role of frequent and long‐term routine surveillance endomyocardial biopsies and comprehensive review of non‐invasive rejection screening tools
Bibliographic record
Abstract
BACKGROUND: Despite significant medical advances in the field of pediatric heart transplantation (HT), acute rejection remains an important cause of morbidity and mortality. Endomyocardial biopsy (EMB) remains the gold-standard method for diagnosing rejection but is an invasive, expensive, and stressful process. Given the potential adverse consequences of rejection, routine post-transplant rejection surveillance protocols incorporating EMB are widely employed to detect asymptomatic rejection. Each center employs their own specific routine rejection surveillance protocol, with no consensus on the optimal approach and with high inter-center variability. The utility of high-frequency and long-term routine surveillance biopsies (RSB) in pediatric HT has been called into question. METHODS: Sources for this comprehensive review were primarily identified through searches in biomedical databases including MEDLINE and Embase. RESULTS: The available literature suggests that the diagnostic yield of RSB is low beyond the first year post-HT and that a reduction in RSB intensity from high-frequency to low-frequency can be done safely with no impact on early and mid-term survival. Though there are emerging non-invasive methods of detecting asymptomatic rejection, the evidence is not yet strong enough for any test to replace EMB. CONCLUSION: Overall, pediatric HT centers in North America should likely be doing fewer RSB than are currently performed. Risk factors for rejection should be considered when designing the optimal rejection surveillance strategy. Noninvasive testing including emerging biomarkers may have a complementary role to aid in safely reducing the need for RSB.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".