Predicting trajectories of lung function decline in systemic sclerosis–related interstitial lung disease
Bibliographic record
Abstract
OBJECTIVE: SSc-related interstitial lung disease (SSc-ILD) is a major cause of morbidity. We aimed to identify patients following similar trajectories of forced vital capacity (FVC) decline, and examine their association with mortality and risk factors for FVC decline. METHODS: This is a multicentre retrospective study of 444 SSc patients with ILD and ≤7-year disease duration. Patients were grouped based on similar FVC decline trajectories using semi-parametric modelling with latent class analysis. Survival was compared between the worst FVC trajectory group and the others. Logistic regression models with backwards selection were applied to identify predictors of FVC trajectory using baseline disease features. RESULTS: Four FVC trajectory groups were identified. The most progressive trajectory declined by -2.18% per year and the other three trajectory groups were stable or progressed slowly. The most progressive group had a higher mortality rate than those with a stable/slow FVC trajectory (hazard ratio 2.95, 95% CI 1.74, 4.98). Baseline FVC (P < 0.001) and CRP elevation (P = 0.039) were associated the progressive trajectory. Baseline FVC ≤72% predicted the progressive trajectory with a sensitivity of 0.88 and specificity of 0.91. A lower baseline FVC was in turn associated with older age, Caucasian race, longer disease duration, anti-topoisomerase I presence and elevated CRP on exploratory analyses. CONCLUSION: Distinct FVC trajectories are associated with different survival outcomes and the most important predictor of a progressive FVC trajectory was existing ILD severity. More work is needed to assess the utility of imaging or paraclinical findings that can improve prediction of distinct FVC trajectories.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".