Characterization of Incident Interstitial Lung Disease in Late Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Interstitial lung disease (ILD) is a common and potentially lethal complication of systemic sclerosis (SSc). Screening by high-resolution computed tomography (HRCT) is recommended in all patients with risk factors, including early disease. Little is known on late presentations of ILD. This study aimed to characterize the incidence, risk factors, and outcomes of late-onset SSc-ILD. METHODS: Study participants enrolled in the Canadian Scleroderma Research Group cohort from 2004 to 2020 without prevalent ILD were included. Incidence and risk factors for ILD (on HRCT) were compared according to disease duration above (late) and below (earlier) seven years from the first non-Raynaud manifestation. Risk of ILD progression was compared using Kaplan-Meier and multivariable Cox models. RESULTS: Overall, 199 (21%) of 969 patients developed incident ILD over a median of 2.4 (interquartile range 1.2-4.3) years. The incidence rate in late SSc (3.7/100 person-years) was lower than in earlier SSc (relative risk 0.68, 95% confidence interval [CI] 0.51-0.92). Risk factors for incident ILD included male sex, diffuse subtype, myositis, antitopoisomerase I autoantibodies, and higher C-reactive protein levels. Patients with late-onset ILD were also less frequently White and more frequently had arthritis and anti-RNA-polymerase III autoantibodies. Lung disease severity was similar between late- and earlier-onset SSc-ILD (forced vital capacity 88% and 87%, diffusion capacity of the lungs for carbon monoxide 64% and 62%, respectively). Progression rates were also similar between late- and earlier-onset SSc-ILD (log rank P = 0.8, hazard ratio 1.11, 95% CI 0.58-2.10). CONCLUSION: ILD can present in late SSc. Risk factors and progression rates overlapped with earlier-onset SSc-ILD. Surveillance for ILD should continue in longstanding SSc. Frequency and modality of monitoring remain to be defined.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".