Acute Cellular Rejection: Is It Still Relevant?
Bibliographic record
Abstract
Despite significant progress in the field of transplant immunology, acute cellular rejection (ACR) remains a very frequent complication after lung transplantation (LTx), with almost 30% of LTx recipients experiencing at least one episode of treated ACR during the first year of follow-up. Most episodes respond to the first-line immunosuppressive treatment and are rarely a direct cause of death. However, the association of ACR with later adverse outcomes, such as chronic lung allograft dysfunction, bronchial stricture, and infectious complications associated with the intensification of immunosuppression, negatively impacts long-term survival. The burden imposed on patients and health-care resources is even higher in cases of refractory or recurrent ACR, which accelerates lung function decline. Although important laboratory and clinical research conducted over the last two decades has improved our understanding of the mechanisms underlying ACR, there are still many uncertainties about the risk factors for ACR, the optimal monitoring strategies, and the prediction of long-term outcomes. These knowledge gaps contribute to the large variability in clinical practice among LTx centers, which renders multicenter studies of ACR challenging. In this review, we summarize current evidence on the epidemiology, pathogenesis, and risk factors of ACR. We describe diagnostic and therapeutic approaches that are currently used in the clinical practice and also review promising diagnostic tools that are under investigation. Associations between ACR and other adverse outcomes of LTx are examined. Finally, within each topic of discussion, we highlight the main areas of controversy and opportunities for future research.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".