Lung histopathology of non‐infectious pulmonary complications after allogeneic haematopoietic stem cell transplantation
Bibliographic record
Abstract
AIMS: Non-infectious pulmonary complications (NIPCs) occur frequently following allogeneic haematopoietic stem cell transplantation (HSCT). As there is no consensus on the description of the related pulmonary pathological lesions, pathologist reports and clinical conclusions are largely inconsistent in routine practice. The aim of our study was to provide an accurate overview of post-allogeneic HSCT NIPCs from a large number of lung biopsies. METHODS AND RESULTS: We reviewed 61 lung biopsies in patients with an NIPC, including 51 surgical lung biopsies, four post-mortem biopsies and six lung explants. We found both bronchiolar (n = 59) and alveolar/interstitial pathologies (n = 27). We describe two types of bronchiolar lesions: bronchiolectasies (n = 37) and fibrous and cellular lesions with luminal narrowing (n = 43). We found a wide spectrum of airway/interstitial pathologies that were labelled using the terminology of the 2013 American Thoracic Society and European Respiratory Society (ATS/ERS) classification of idiopathic interstitial pneumonias (IIPs), including the following: organising pneumonia (OP, n = 8), non-specific interstitial pneumonia (NSIP, n = 9), diffuse alveolar damage (DAD, n = 6), lymphoid interstitial pneumonia (LIP, n = 1) and pleuroparenchymal fibroelastosis (PPFE, n = 2), as well as one instance of associated PPFE and NSIP. CONCLUSIONS: Interstitial pathology was associated with bronchiolar lesions in 41% of the cases reviewed (n = 25). Lung airway and interstitial inflammation was still present in lung explants from patients who underwent lung transplantation for post-allogeneic HSCT end-stage respiratory insufficiency. Herein, we describe a wide spectrum of pathological lung lesions encountered in post-allogeneic HSCT NIPCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".