An interim report of the Scleroderma Clinical Trials Consortium working groups
Bibliographic record
Abstract
The Scleroderma Clinical Trials Consortium (SCTC) represents many of the clinical researchers in the world who are interested in improving the efficiency of clinical trials in Systemic Sclerosis (SSc). The SCTC has established 11 working groups (WGs) to develop and validate better ways of measuring and recording multiple aspects of this heterogeneous disease. These include groups working on arthritis, disease damage, disease activity, cardiac disease, juvenile SSc, the gastrointestinal tract, vascular component, calcinosis, scleroderma renal crisis, interstitial lung disease, and skin measurement. Members of the SCTC may join any one or more of these groups. Some of the WGs have only recently started their work, some are nearing completion of their mandated tasks and others are in the midst of their projects. All these projects, which are described in this paper, will help to improve clinical trials and observational studies by improving or developing better, more sensitive ways of measuring various aspects of the disease. As Lord Kelvin stated, "To measure is to know. If you cannot measure it you cannot improve it." The SCTC is dedicated to improving the lives of patients with SSc and it is our hope that the contributions of the WGs will be one important step in this process.
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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 | 0.376 | 0.248 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.029 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".