A Brief Guide to Interpreting Transbronchial Cryobiopsies for Diffuse Parenchymal Lung Disease
Bibliographic record
Abstract
Transbronchial cryobiopsies (CB) are increasingly replacing surgical biopsies (video-assisted thoracoscopic/VATS biopsies) for diagnosing diffuse parenchymal lung disease (interstitial lung disease, ILD), but there is very little guidance for pathologists on CB interpretation. Here we propose a fairly simple approach. First, if the diagnosis can be made on a traditional forceps biopsy, it can be made on a cryobiopsy. Many diseases with specific features will fall into this category (eg, sarcoidosis or Langerhans cell histiocytosis). More problematic are patterns such as usual interstitial pneumonia (UIP) or nonspecific interstitial pneumonia (NSIP), in which low-power architecture is the key to diagnosis. In this circumstance, an adequate sample is crucial to look for features such as fibroblast foci, because a combination of fibroblast foci plus any patchy old fibrosis, fibrotic architectural remodeling, or honeycombing, allows a diagnosis of a UIP pattern. However, in most instances, CB will not separate the UIP patterns seen in idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis, or connective tissue disease-interstitial lung disease (CTD-ILD), although giant cells/granulomas (uncommon findings) in this setting favor fibrotic hypersensitivity pneumonitis. Fibroblast foci can be difficult to differentiate from organizing pneumonia (OP), but granulation tissue plugs clearly in airspaces favor OP. Absent fibroblast foci, patchy old fibrosis, architectural distortion, and honeycombing by themselves do not allow a specific diagnosis. NSIP in CB microscopically looks like NSIP in VATS biopsies, and the presence of an NSIP or an NSIP+OP pattern is typical of CTD-ILD. All the above diagnoses require correlation with clinical and radiologic findings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.066 |
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".