Chronic Hypersensitivity Pneumonitis: Differentiation from Idiopathic Pulmonary Fibrosis and Nonspecific Interstitial Pneumonia by Using Thin-Section CT
Bibliographic record
Abstract
PURPOSE: To retrospectively assess the accuracy of thin-section computed tomography (CT) in distinguishing chronic hypersensitivity pneumonitis (HP) from idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonia (NSIP), with histologic results as the reference standard. MATERIALS AND METHODS: This retrospective study was approved by the institutional research boards of the participating centers, and informed consent was waived. There was HIPAA compliance for all U.S. patients. The study included 66 patients (36 men, 30 women; mean age, 58.8 years +/- 10.9 [standard deviation]) with proved chronic HP (n = 18), IPF (n = 23), or NSIP (n = 25) who underwent CT. Two independent readers assessed the CT images, made a first-choice diagnosis, and noted the degree of confidence in the diagnosis. A general linear model was used to identify CT features that independently differentiated chronic HP from IPF and NSIP. Weighted kappa statistic was used to assess interobserver agreement. RESULTS: The CT features that best differentiated chronic HP were lobular areas with decreased attenuation and vascularity, centrilobular nodules, and absence of lower zone predominance of abnormalities (P < or = .008). The features that best differentiated NSIP were relative subpleural sparing, absence of lobular areas with decreased attenuation, and lack of honeycombing (P < or = .002). The features that best differentiated IPF were basal predominance of honeycombing, absence of relative subpleural sparing, and absence centrilobular nodules (P < or = .004). A confident diagnosis was made in 70 (53%) of 132 readings. This diagnosis was correct in 66 (94%) of 70 readings. The accuracy for the entire cohort was 80%. Interobserver agreement for confident diagnosis was good to excellent (kappa = 0.77-0.96). CONCLUSION: Characteristic CT features of chronic HP, IPF, and NSIP allow confident distinction between these entities in approximately 50% of patients.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".