Chronic hypersensitivity pneumonitis: real world diagnostic criteria
Bibliographic record
Abstract
PURPOSE OF REVIEW: Establishing a diagnosis of hypersensitivity pneumonitis (HP) and distinguishing it from other forms of interstitial lung diseases represents a common challenge in clinical practice. This review summarizes the latest literature and guidelines on HP while integrating some real-life conundrums. RECENT FINDINGS: Advances in the understanding of the pathobiology of fibrotic HP and other progressive pulmonary fibrosis have changed how we approach the diagnosis and treatment of interstitial lung disease. Classifications now embrace distinguishing two clinical phenotypes: nonfibrotic and fibrotic HP because of distinct disease behavior and prognosis implications. International guidelines on HP were recently published and proposed a framework and algorithm to guide the diagnostic process. SUMMARY: The diagnosis of HP relies on the integration of multiples domains: clinical assessment of exposure, imaging, bronchoalveolar lavage lymphocytosis and histopathological findings. These features are reviewed in multidisciplinary discussion and lead to an estimation of the degree of confidence for HP diagnosis. Further research is warranted to improve knowledge on the pathophysiology of HP and ultimately improve its diagnostic approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".