The challenges and controversies of measuring disease activity in systemic sclerosis
Bibliographic record
Abstract
Major alteration of the natural history of systemic sclerosis is limited with current treatments, and the development of novel therapies has been hampered, in part, by the lack of fully validated multi-system outcome measures. There remains a lack of consensus as to the very definition of systemic sclerosis disease activity, complicating efforts to measure activity in clinical trials. Previously published multi-system measures of disease status are yet to be fully validated according to the Outcome Measures in Rheumatology (OMERACT) filter. There is currently significant research interest in developing new systemic sclerosis-specific measures to better describe and compare patient cohorts and measure therapeutic responses in clinical trials. An accurate measure of disease activity in systemic sclerosis will facilitate the enrichment of clinical trials with patients who have active disease, targeting a group of patients most likely to benefit from therapeutic intervention. In addition, following on from successes in other rheumatic conditions, a state of low disease activity, measured by an activity index, may become a clinical trial end point and therapeutic target. The Scleroderma Clinical Trials Consortium has undertaken to develop a definition of disease activity and fully validate a new systemic sclerosis activity index. The Scleroderma Clinical Trials Consortium Activity Index will be developed using consensus and data-driven methods and is envisaged to be widely used in research and clinical settings.
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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 | 0.046 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.048 |
| Insufficient payload (model declined to judge) | 0.002 | 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".