Worldwide Expert Agreement on Updated Recommendations for the Treatment of Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate agreement of the updated European League Against Rheumatism and European Scleroderma Trials and Research group (EUSTAR) recommendations for treatment of systemic sclerosis (SSc) among international experts. In addition, to determine factors that might influence agreement. METHODS: Level of agreement (10-point scale: 0 = not at all, 10 = completely agree) and local drug availability (yes/no) were assessed using an online survey. The Web link to the survey was shared with 481 unique e-mail addresses and SSc networks (Scleroderma Clinical Trials Consortium, Australian Scleroderma Interest Group, International Systemic Sclerosis Inception Cohort). Level of agreement was compared between subgroups stratified for participant characteristics. RESULTS: In total, 263 experts participated, of whom n = 209 (79%) completed each single item. The majority were rheumatologists (n = 200, 76%) working in Europe (n = 185; 71%); 59% (n = 156) were EUSTAR members; and 57% (n = 151) had > 10 years of clinical experience. Overall level of agreement was high (mean 8.0, SD 2.5). The 3 highest mean agreements included (1) angiotensin-converting enzyme inhibitors for scleroderma renal crisis (9.2, SD 2.1); (2) blood pressure control in SSc-patients treated with corticosteroids (9.0, SD 2.2); (3) proton pump inhibitors to prevent reflux complications (9.0, SD 2.2). The 3 lowest mean agreements included (1) fluoxetine for Raynaud phenomenon (RP; 4.6, SD 2.8); (2) hematopoietic stem cell transplantation (HSCT) for severe SSc (7.1, SD 2.9); (3) phosphodiesterase inhibitors 5 for RP (7.3, SD 2.7). Agreement differed between Europe and non-Europe for the use of iloprost, bosentan, methotrexate, HSCT, and cyclophosphamide. Treatment availability could partially explain differential agreement for iloprost, bosentan, and HSCT. CONCLUSION: In general, worldwide expert agreement on updated recommendations for treatment of SSc is high, supporting their value. Differences in agreement are partially explained by geographical area and treatment availability.
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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 | 0.248 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".