Centrilobular Fibrosis in Fibrotic (Chronic) Hypersensitivity Pneumonitis, Usual Interstitial Pneumonia, and Connective Tissue Disease–Associated Interstitial Lung Disease
Bibliographic record
Abstract
CONTEXT.—: Various pulmonary diseases can produce centrilobular (peribronchiolar) fibrosis, which may be isolated or associated with other patterns of more diffuse fibrosis. The major forms of interstitial lung disease in which centrilobular fibrosis is found are fibrotic (chronic) hypersensitivity pneumonitis, connective tissue disease-associated interstitial lung disease, and (a disputed issue) usual interstitial pneumonia/idiopathic interstitial fibrosis. OBJECTIVE.—: To review recent literature that addresses separation of these entities. DATA SOURCES.—: Data comprised recent publications. CONCLUSIONS.—: In a specially constructed multidisciplinary discussion exercise, it was found that peribronchiolar metaplasia affecting more than half the bronchioles or more than 2 foci of peribronchiolar metaplasia per square centimeter of biopsy area was strongly associated with a confident diagnosis of fibrotic hypersensitivity pneumonitis. Giant cells or granulomas were only found in cases with a greater than 50% diagnostic confidence in hypersensitivity pneumonitis. Conversely, greater numbers of fibroblast foci per square centimeter and increasing measured amounts of subpleural fibrosis favored a diagnosis of usual interstitial pneumonia. Recent data also suggest that centrilobular fibrosis can be found in usual interstitial pneumonia, although the presence of centrilobular fibrosis statistically favors an alternate diagnosis. Connective tissue disease is a major confounder because many patterns are very similar to fibrotic hypersensitivity pneumonitis or usual interstitial pneumonia. Genetic abnormalities, such as the MUC5B minor allele overlap, in these conditions and at this point cannot be used for discrimination. Thus, the separation of fibrotic hypersensitivity pneumonitis and usual interstitial pneumonia remains a difficult problem. Accurate biopsy diagnosis of all of these diseases requires correlation with imaging and clinical findings, and is crucial for treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".