Disease modification and other trials in systemic sclerosis have come a long way, but have to go further
Bibliographic record
Abstract
In this issue of Arthritis Care & Research, the View article by Mendoza et al about clinical trial design in systemic sclerosis (SSc; scleroderma) suggests that there cannot be a universal design for clinical trials of disease modification in SSc because the disease has so many different presentations (1).However, it is still important in any randomized controlled trial (RCT) to use proper statistics, including sample size calculations, to be able to recruit subjects (feasible), and ideally to have subjects enrolled that are similar to SSc patients with comparable organ-based disease activity (generalizable).There will not be a single design for every trial, but basic principles must be adhered to.We have written this article as a counterview to the views by Mendoza et al (1), but most of the opinions are in agreement.The headings are ordered in a way that would mimic the process of clinical trial design and conduct (general principles, end points, and discussion of organspecific trials in SSc skin and lung).Examples from trials of various rheumatic diseases have been provided to illustrate various aspects of trial design.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.062 | 0.052 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".