Pathological features of connective tissue disease‐associated interstitial lung disease in transbronchial cryobiopsies
Bibliographic record
Abstract
Aim Transbronchial cryobiopsies are increasingly used for the diagnosis of interstitial lung disease (ILD), but there is a lack of published information on the features of specific ILD in cryobiopsies. Here we attempt to provide pathological guidelines for separating usual interstitial pneumonia (UIP) of idiopathic pulmonary fibrosis (IPF), fibrotic hypersensitivity pneumonitis (FHP) and connective tissue disease‐associated ILD (CTD–ILD) in cryobiopsies. Methods We examined 120 cryobiopsies from patients with multidisciplinary discussion (MDD)‐established CTD–ILD and compared them to a prior series of 121 biopsies from patients with MDD‐established IPF or FHP. Results A non‐specific interstitial pneumonia (NSIP) pattern alone was seen in 36 of 120 (30%) CTD–ILD, three of 83 (3.6%) FHP and two of 38 (5.2%) IPF cases, statistically favouring a diagnosis of CTD–ILD. The combination of NSIP + OP was present in 29 of 120 (24%) CTD–ILD, two of 83 (2.4%) FHP and none of 38 (0%) IPF cases, favouring a diagnosis of CTD–ILD. A UIP pattern, defined as fibroblast foci plus any of patchy old fibrosis/fibrosis with architectural distortion/honeycombing, was identified in 28 of 120 (23%) CTD–ILD, 45 of 83 (54%) FHP and 27 of 38 (71%) IPF cases and supported a diagnosis of FHP or IPF. The number of lymphoid aggregates/mm2 and fibroblast foci/mm2 was not different in IPF, CTD–ILD or FHP cases with a UIP pattern. Interstitial giant cells supported a diagnosis of FHP or CTD–ILD over IPF, but were infrequent. Conclusions In the correct clinical/radiological context the pathological findings of NSIP, and particularly NSIP plus OP, favour a diagnosis of CTD–ILD in a cryobiopsy, but CTD–ILD with a UIP pattern, FHP with a UIP pattern and IPF generally cannot be distinguished.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".