Demographic and clinical predictors of progression and mortality in connective tissue disease-associated interstitial lung disease: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Connective tissue disease-associated interstitial lung disease (CTD-ILD) is associated with reduced quality of life and poor prognosis. Prior studies have not identified a consistent combination of variables that accurately predict prognosis in CTD-ILD. The objective of this study was to identify baseline demographic and clinical characteristics that are associated with progression and mortality in CTD-ILD. METHODS: Patients were retrospectively identified from an adult CTD-ILD clinic. The predictive significance of baseline variables on serial forced vital capacity (FVC), diffusion capacity (DLCO), and six-minute walk distance (6MWD) was assessed using linear mixed effects models, and Cox regression analysis was performed to assess impact on mortality. RESULTS: 359 patients were included in the study. Median follow-up time was 4.0 (IQR 1.5-7.6) years. On both unadjusted and multivariable analysis, male sex and South Asian ethnicity were associated with decline in FVC. Male sex, positive smoking history, and diagnosis of systemic sclerosis (SSc) vs. other CTD were associated with decline in DLCO. Male sex and usual interstitial pneumonia (UIP) pattern predicted decline in 6MWD. There were 85 (23.7%) deaths. Male sex, older age, First Nations ethnicity, and a diagnosis of systemic sclerosis vs. rheumatoid arthritis were predictors of mortality on unadjusted and multivariable analysis. CONCLUSION: Male sex, older age, smoking, South Asian or First Nations ethnicity, and UIP pattern predicted decline in lung function and/or mortality in CTD-ILD. Further longitudinal studies may add to current clinical prediction models for prognostication in CTD-ILD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".