Clinical approach to acute cellular rejection from the pediatric heart transplant society
Bibliographic record
Abstract
BACKGROUND: Early detection of cardiac allograft rejection is crucial for post-transplant graft survival. Despite the progress made in immunosuppression strategies, acute cellular rejection remains a serious complication during and after the first post-transplant year, and there is a continued lack of consensus regarding its treatment, especially in pediatric transplant patients. METHODS: An open request was placed via the listserv to the membership of the Pediatric Heart Transplant Society (PHTS). Along with a broad literature search, numerous institutional protocols were pooled, analyzed and consolidated. A clinical approach document was generated highlighting areas of consensus and practice variation. RESULTS: The clinical approach document divides cellular rejection by International Society for Heart and Lung Transplantation grades and provides management strategies for each, including persistent cellular rejection. CONCLUSIONS: Cellular rejection treatment can be tailored to the clinical status, graft function, and the grade of cellular rejection. A case of mild and asymptomatic rejection may not require treatment, whereas a higher-grade rejection or rejection with graft dysfunction or hemodynamic compromise may require aggressive intravenous therapies, changes to maintenance immunosuppression therapy and augmented surveillance.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".