Screening and management of subclinical interstitial lung disease in systemic sclerosis: an international survey
Bibliographic record
Abstract
OBJECTIVE: Interstitial lung disease (ILD) is the leading cause of mortality in SSc. Experts now recommend high-resolution CT (HRCT) screening in all SSc patients and treatment of subclinical ILD in SSc patients with high-risk phenotypes. We undertook an international survey to understand current screening and treatment practices in subclinical SSc-ILD. METHODS: An electronic REDCap survey was distributed to 611 general rheumatologists, 348 national and international SSc experts, 285 general respirologists and 57 ILD experts. RESULTS: One hundred and ninety-eight participants responded to the survey, including 135 (68%) rheumatologists and 54 (27%) respirologists. Over half (59%) of respondents routinely ordered HRCTs in all newly diagnosed SSc patients, although this practice was more common in Europe (83%), the USA (68%), Asia (73%) and Latin America (100%) compared with Canada (40%) and Australia (40%). Nearly half (48%) of respondents would not treat subclinical SSc-ILD, whereas 52% would treat or consider treatment. At least 70% would likely treat subclinical ILD in the setting of diffuse SSc, anti-topoisomerase-I autoantibodies, disease duration below 18 months, ground-glass opacities, oxygen desaturation, or significant ILD progression on imaging or pulmonary function tests. The majority (67%) of respirologists would not treat subclinical ILD. MMF was the preferred first-line drug for the treatment of subclinical SSc-ILD. CONCLUSION: This international survey highlights important regional variations in SSc-ILD screening and significant heterogeneity among rheumatologists and respirologists in the treatment of subclinical SSc-ILD. High-quality research addressing these questions is needed to produce evidence-based guidelines and harmonize the approach to identification and treatment of subclinical SSc-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 | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".