Management of fibrotic hypersensitivity pneumonitis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent guidelines have updated the classification of hypersensitivity pneumonitis, stratifying by the presence or absence of fibrosis as either fibrotic or nonfibrotic hypersensitivity pneumonitis. Fibrotic hypersensitivity pneumonitis represents up to 10% of interstitial lung disease in large cohort studies, and is occasionally even more common in some regions; however, there are many unknown aspects to the diagnosis and management. The goal of this review article is to summarize the management of fibrotic hypersensitivity pneumonitis. RECENT FINDINGS: Historically, the only treatment options for patients with hypersensitivity pneumonitis were antigen avoidance and corticosteroids, although other immunosuppressive therapies are increasingly endorsed by experts in the field. There is accumulating evidence that antifibrotic medications can be useful as a second-line therapy in some patients with fibrotic hypersensitivity pneumonitis who have progression despite immunosuppression. There remains no direct comparison of immunosuppressive vs. antifibrotic medication for the management of fibrotic hypersensitivity pneumonitis, but some clinical, radiological and pathological features may suggest greater likelihood of benefit from one option or the other. SUMMARY: We anticipate that future treatment of fibrotic hypersensitivity pneumonitis will consider a variety of patient features to suggest the most prominent underlying biology that will then be used to guide initial pharmacotherapy; however, additional data are still needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".