Morphologic Features of Fibrotic Hypersensitivity Pneumonitis in Transbronchial Cryobiopsies Versus Video-Assisted Thoracoscopic Biopsies: An In Silico Study
Bibliographic record
Abstract
CONTEXT.—: There is interest in using transbronchial cryobiopsies (CBs) for the diagnosis of fibrotic (chronic) hypersensitivity pneumonitis (FHP), but with little information in the literature about what features are diagnostic in CBs. OBJECTIVE.—: To determine, using in silico investigation, whether features supporting a diagnosis of FHP in video-assisted thoracoscopic (VATS) biopsies can be identified in CBs. DESIGN.—: In silico circular "cryobiopsies," 5.25 mm in diameter (21.6 mm2), were created on the slides of 15 VATS biopsy cases that had been assigned a 60% or greater confident diagnosis of FHP at a specially devised multidisciplinary discussion. Using stratified random sampling, up to 8 "cryobiopsies" per case were analyzed for the presence of giant cells/granulomas or peribronchiolar metaplasia affecting 50% or more of the bronchioles, features that had statistically supported a diagnosis of FHP on the VATS biopsies in the multidisciplinary discussion exercise. RESULTS.—: Giant cells/granulomas were detected with very low sensitivities in the "cryobiopsies." Using peribronchiolar metaplasia in 50% or more of bronchioles alone, the sensitivity/specificity for a diagnosis of FHP of 2 "cryobiopsies" compared to the corresponding VATS biopsy was 0.57/0.63; for 4 "cryobiopsies," 0.86/0.75; and for 8 "cryobiopsies," 0.83/0.71. Adding giant cells/granulomas slightly improved these numbers to 0.63/0.71 for 2 "cryobiopsies"; 1.00/0.86 for 4; and 1.00/0.80 for 8. CONCLUSIONS.—: In the setting of a multidisciplinary discussion where FHP is part of the differential diagnostic choices, 4 actual CBs with an area of roughly 20 mm2 each should have good sensitivity and reasonable specificity for diagnosing FHP using these specific morphologic criteria.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".