Diagnosis of Acute Cellular Rejection and Antibody-Mediated Rejection on Lung Transplant Biopsies: A Perspective From Members of the Pulmonary Pathology Society
Bibliographic record
Abstract
CONTEXT: - The diagnosis and grading of acute cellular and antibody-mediated rejection (AMR) in lung allograft biopsies is important because rejection can lead to acute graft dysfunction and/or failure and may contribute to chronic graft failure. While acute cellular rejection is well defined histologically, no reproducible specific features of AMR are currently identified. Therefore, a combination of clinical features, serology, histopathology, and immunologic findings is suggested for the diagnosis of AMR. OBJECTIVE: - To describe the perspective of members of the Pulmonary Pathology Society (PPS) on the workup of lung allograft transbronchial biopsy and the diagnosis of acute cellular rejection and AMR in lung transplant. DATA SOURCES: - Reports by the International Society for Heart and Lung Transplantation (ISHLT), experience of members of PPS who routinely review lung allograft biopsies, and search of literature database (PubMed). CONCLUSIONS: - Acute cellular rejection should be assessed and graded according to the 2007 working formulation of the ISHLT. As currently no specific features are known for AMR in lung allografts, the triple test (clinical allograft dysfunction, donor-specific antibodies, pathologic findings) should be used for its diagnosis. C4d staining might be performed when morphologic, clinical, and/or serologic features suggestive of AMR are identified.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".