Natural variability in the disease course of SSc-ILD: implications for treatment
Bibliographic record
Abstract
Interstitial lung disease (ILD) affects approximately 50% of patients with systemic sclerosis (SSc) and is the leading cause of death in SSc. Our objective was to gain insight into the progression of SSc-associated ILD (SSc-ILD). Using data from longitudinal clinical trials and observational studies, we assessed definitions and patterns of progression, risk factors for progression, and implications for treatment. SSc-ILD progression was commonly defined as exceeding specific thresholds of lung function worsening and/or increasing radiographic involvement. One definition used in several studies is decline in forced vital capacity (FVC) of ≥10%, or ≥5-10% plus a decline in diffusing capacity of the lung for carbon monoxide ≥15%. Based on these criteria, 20-30% of patients in observational cohorts develop progressive ILD, starting early in the disease course and progressing at a highly variable rate.Risk factors such as age, FVC, extent of fibrosis and presence of anti-topoisomerase I antibodies can help predict progression of SSc-ILD, though composite risk scores may offer greater predictive power. Whilst the variability of the disease course in SSc-ILD makes risk stratification of patients challenging, the decision to initiate, change or stop treatment should be based on a combination of the current disease state and the speed of progression.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".