Diagnosis of Hypersensitivity Pneumonitis
Bibliographic record
Abstract
Assessment of lung biopsies for the diagnosis of hypersensitivity pneumonitis (HP) is one of the most difficult diagnostic problems for surgical pathologists. It is a form of interstitial lung disease resulting from an immune reaction provoked by an inhaled antigen in susceptible individuals. Although this definition sounds simple, in practice, the diagnosis of HP can be challenging. To address these issues, the American College of Chest Physicians (CHEST) has recently published a guideline for the diagnosis of HP. In this review, we will explore the multidisciplinary diagnostic evaluation of HP with a focus on the pathologic features as outlined in the CHEST guidelines. The histologic criteria are divided into 4 diagnostic categories: (1) Typical nonfibrotic HP or fibrotic HP; (2) Compatible with nonfibrotic HP or fibrotic HP; (3) Indeterminate for nonfibrotic or fibrotic HP; and (4) Alternative Diagnosis. It is important to emphasize that patterns 1 to 3 do not represent discrete histologic entities or pathologic diagnoses. Rather, these categories are meant to serve as a practical guide for organizing a complex set of overlapping histologic patterns into an integrated diagnostic framework for facilitating multidisciplinary discussion. High-resolution computed tomography features are also summarized, emphasizing how the correlation of lung biopsies with computed tomography findings can help to favor the diagnosis, particularly in cases where biopsies are not typical for HP. This review highlights details of the histologic spectrum of HP as well as the utility of different types of biopsies and bronchoalveolar lavage. We also emphasize the importance of multidisciplinary discussion and the complex differential diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".