An Analysis of Connective Tissue Disease-associated Interstitial Lung Disease at a US Tertiary Care Center: Better Survival in Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To compare survival of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) versus idiopathic pulmonary fibrosis (IPF) and patients with systemic sclerosis-associated ILD (SSc-ILD) versus other CTD-ILD followed at our center. METHODS: We used the Stanford ILD database, which contains prospectively collected information on patients with ILD evaluated at our tertiary care center from 2002 to 2009. Survival at last followup from time of ILD diagnosis was calculated using the Kaplan-Meier estimator. Prognostic factors for survival in the overall cohort (IPF and CTD-ILD) and in the CTD-ILD group were identified with univariate and multivariate Cox regression models. RESULTS: Of 427 patients with ILD, 148 (35%) had IPF and 76 (18%) had CTD-ILD at the baseline visit. The cumulative incidence of CTD was 4%. After a median followup of 4 years, 67 patients (36.4%) had died and 4 (2.2%) were lost to followup. Patients with IPF (n = 122) and CTD-ILD (n = 62) experienced similar survival rates (5-year survival about 50%). Patients with SSc-ILD (n = 24) experienced better survival than those with other CTD-ILD (n = 38), with 1-year, 3-year, and 5-year survival rates of 100%, 90%, and 77%, respectively, versus 78%, 42%, and 38% (p = 0.01). The presence of SSc in patients with CTD-ILD decreased the risk of death by > 80% even after correcting for age at ILD diagnosis, sex, and ethnicity (HR = 0.17, 95% CI 0.04-0.83). CONCLUSION: Survival in patients with SSc-ILD was better than in patients with other CTD-ILD, potentially related to routine screening for and early detection of ILD in patients with SSc at our center.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".