Lung imaging patterns in connective tissue disease–associated interstitial lung disease impact prognosis and immunosuppression response
Bibliographic record
Abstract
OBJECTIVES: Interstitial lung disease (ILD) in CTDs has highly variable morphology. We aimed to identify imaging features and their impact on ILD progression, mortality, and immunosuppression response. METHODS: Patients with CTD-ILD had high-resolution chest CT (HRCT) reviewed by expert radiologists blinded to clinical data for overall imaging pattern [usual interstitial pneumonia (UIP); non-specific interstitial pneumonia (NSIP); organizing pneumonia (OP); fibrotic hypersensitivity pneumonitis (fHP); and other]. Transplant-free survival and change in percent-predicted forced vital capacity (FVC) were compared using Cox and linear mixed-effects models adjusted for age, sex, smoking, and baseline FVC. FVC decline after immunosuppression was compared with pre-treatment. RESULTS: Among 645 CTD-ILD patients, the most frequent CTDs were SSc (n = 215), RA (n = 127), and inflammatory myopathies (n = 100). NSIP was the most common pattern (54%), followed by UIP (20%), fHP (9%), and OP (5%). Compared with the case for patients with UIP, FVC decline was slower in patients with NSIP (by 1.1%/year, 95% CI 0.2, 1.9) or OP (by 3.5%/year, 95% CI 2.0, 4.9), and mortality was lower in patients with NSIP [hazard ratio (HR) 0.65, 95% CI 0.45, 0.93] or OP (HR 0.18, 95% CI 0.05, 0.57), but higher in fHP (HR 1.58, 95% CI 1.01, 2.40). The extent of fibrosis also predicted FVC decline and mortality. After immunosuppression, FVC decline was slower compared with pre-treatment in NSIP (by 2.1%/year, 95% CI 1.4, 2.8), with no change for UIP or fHP. CONCLUSION: Multiple radiologic patterns are possible in CTD-ILD, including a fHP pattern. NSIP and OP were associated with better outcomes and response to immunosuppression, while fHP had worse survival compared with UIP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".