Recommendations for the Treatment of Systemic Sclerosis: Agreement May Not Translate into Uptake
Bibliographic record
Abstract
In this issue of The Journal , de Vries-Bouwstra, et al evaluated the level of agreement for the recommendations for systemic sclerosis (SSc; scleroderma) treatment from the European League Against Rheumatism (EULAR) Scleroderma Trials and Research group (EUSTAR)1,2. They studied the level of agreement on an 11-point scale (from 0 no agreement, to 10 full agreement) and surveyed 481 SSc experts from various organizations, most of whom had more than a decade of experience in the treatment of SSc. The response rate was about 55%, reasonable for this type of survey1. They found most items had a high level of agreement. Not surprisingly, there was high agreement in areas beyond debate, such as treating scleroderma renal crisis with angiotensin-converting enzyme inhibitors, and using proton pump inhibitors to treat symptoms of gastroesophageal reflux disease (GERD) and prevent GERD complications. There was also high agreement that patients with SSc who were using corticosteroids should have blood pressure and renal function monitored. This would be especially important in the diffuse cutaneous SSc (dcSSc) subset who are positive for anti-RNA polymerase antibody3, but many laboratories do not measure this antibody. … Address correspondence to Dr. J.E. Pope, Division of Rheumatology, St. Joseph’s Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada. E-mail: janet.pope{at}sjhc.london.on.ca
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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 | 0.138 | 0.444 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".