Pathologists’ experience and routine practice in high-volume lung transplant centers: An international survey
Bibliographic record
Abstract
Lung allograft pathology encompasses a wide spectrum of disorders, with new entities and emerging diagnostic technologies. The main goal of this study was to document pathologists' current global practices and highlight areas of consensus and divergence worldwide. A 24-item survey was distributed to transplant centers and responses were received from 35 specialists (51% European, 49% non-European). Most centers used both surveillance and symptom-driven assessments (77%), mainly with transbronchial biopsies (86%) and bronchoalveolar lavage (83%). Non-European centers were more likely to adopt digital pathology (OR 3.03), to use donor-derived cell-free DNA (dd-cfDNA) (OR 2.94), and supported large airway sampling (OR 2.27). Larger centers used dd-cfDNA (OR 2.75) more frequently and consider large airway sampling (OR 2.18). Responders largely supported standardized reporting (86%), reintroducing grade AX (94%), and independent bronchial lesion scoring (82%). Most endorsed an "indeterminate" category for acute and chronic rejection. These findings reflect evolving practices in the field and will inform the ongoing revision of the Lung Allograft Pathology Working Classification.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".