Features and Outcomes of Patients With a Typical Fibrotic Hypersensitivity Pneumonitis Pattern on Chest Computed Tomography
Bibliographic record
Abstract
Abstract Background: Guidelines have defined “typical hypersensitivity pneumonitis (HP)” imaging patterns for fibrotic HP (fHP); however, the frequency, characteristics, and outcomes of different multidisciplinary diagnoses within this pattern are unknown. Methods: Patients with a typical fHP pattern on chest computed tomography (CT) were identified from a prospective registry. Multidisciplinary diagnoses were established by consensus during a standardized multidisciplinary discussion of all available data. Pre-specified diagnostic categories of interest included fHP with an exposure identified, fHP without an exposure identified, and connective tissue disease-associated interstitial lung disease (CTD-ILD), with each diagnosis defined by >50% likelihood after multidisciplinary discussion. Clinical and radiological features and outcomes were compared across multidisciplinary diagnoses. Results: Of 164 patients with a CT pattern of typical fHP, 49 had a multidisciplinary diagnosis of fHP with a probable or possible exposure identified (30%), 56 had fHP without an exposure (34%), 36 had a CTD-ILD (22%), and 23 had another multidisciplinary diagnosis (14%) (Figure). Clinical and CT features differed across multidisciplinary diagnoses. Lung function decline and time to death or transplant were worse in fHP without a probable or possible exposure. Positive autoimmune serologies or a new rheumatologist-confirmed CTD diagnosis developed in 14% of patients with fHP without an exposure identified during follow-up of at least 4 years. Conclusion: Patients with a typical fHP pattern on CT frequently have non-HP diagnoses (most often CTD-ILD), have differences in baseline characteristics and disease behavior across multidisciplinary diagnoses, and more frequently develop features of CTD during follow-up when an initial HP exposure is not identified.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".