Setting the international standard for longitudinal follow-up of patients with systemic sclerosis: a Delphi-based expert consensus on core clinical features
Bibliographic record
Abstract
Background: Systemic sclerosis (SSc) is a severe, progressive multiorgan disease but to date, there are no established standardised international guidelines for follow-up of patients with SSc. The goal of this project was to develop an expert consensus for annual systematic investigations in patients with SSc to enhance their standard-of-care. Material and methods: The Delphi method was applied. All SSc experts from the European Scleroderma Trials and Research group network and the Scleroderma Clinical Trial Consortium were invited to participate. All experts were asked to answer questionnaires in five Delphi steps to determine the domains of interest and tools for each domain for an annual systematic assessment of patients with SSc. Each item was rated on a scale between 0% and 100% (not and very important), and parameters rated >80% by more than 75% of the experts were regarded as acceptable. Results: In total, 157 experts worldwide participated with 71.3% experts seeing >50 patients with SSc annually. In the first round, 23 domains and 204 tools were suggested. After five Delphi steps, experts agreed on 10 domains including (1) Raynaud's phenomenon; (2) Digital ulcers; (3) Skin and mucosa; (4) Lung; (5); Heart; (6) GI domain, (7) Renal; (8) Musculoskeletal; (9) Laboratory and (10) Treatment. Overall, 55 tools were identified including clinical assessments, laboratory measurements and imaging or functional investigations. Conclusion: Through five Delphi steps with world leading experts, a consensus was established on strongly suggested tools for a minimum annual systemic assessment of organ involvement in SSc. This work should enhance the standardisation and homogenisation of the practices.
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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 | 0.315 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".