Treat-to-target Endpoint Definitions in Systemic Lupus Erythematosus: More Is Less?
Bibliographic record
Abstract
The use of treat-to-target (T2T) strategies has revolutionized the treatment of rheumatoid arthritis (RA) and other chronic diseases such as hypertension and diabetes. Evidence that attainment of a prespecified endpoint is associated with improved longterm outcomes dates to the TICORA study of 2003, which used a prespecified definition of low disease activity1. From that auspicious beginning of T2T in rheumatology, we have now come full circle. For example, the American College of Rheumatology (ACR)/European League Against Rheumatism definition of remission in RA was grounded on empirical analysis of factors contributing to improved outcomes2. These prospectively derived and comprehensively validated endpoints form the basis of RA treatment guidelines that are now routinely applied in clinical practice3, contributing to a transformation in outcomes for patients with RA that includes significant improvements in survival4. Sadly, in systemic lupus erythematosus (SLE) the same story cannot yet be told. In the same 2 decades in which profound mortality improvements in RA were observed4, there has been virtually no improvement in mortality in SLE5, and only 1 novel target therapy has been approved6. Many trials of targeted therapies have been done, but have failed; reasons for the shortage of breakthrough medicines for SLE include the clinical and biological heterogeneity of the disease. However, a lack of well-validated endpoints is certainly a contributory factor to the recurrent failure of clinical trials in SLE; a report in January 2019 in Nature Biotechnology 7 highlighted the lack of validated endpoints for trials in SLE as a “crisis.” T2T studies such … Address correspondence to E.F. Morand, Monash Medical Centre, 246 Clayton Road, Clayton 3168, Melbourne, Australia. E-mail: eric.morand{at}monash.edu
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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 | 0.377 | 0.400 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.014 | 0.058 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".