Conceptual framework for defining disease modification in systemic lupus erythematosus: a call for formal criteria
Bibliographic record
Abstract
Disease modification has become a well-established concept in several therapeutic areas; however, no widely accepted definition of disease modification exists for SLE.We reviewed established definitions of disease modification in other conditions and identified a meaningful effect on 'disease manifestations' (ie, signs, symptoms and patient-reported outcomes) and on 'disease outcomes' (eg, long-term remission or progression of damage) as the key principles of disease modification, indicating a positive effect on the natural course of the disease. Based on these findings and the treatment goals and outcome measures for SLE, including lupus nephritis, we suggest a definition of disease modification based on disease activity indices and organ damage outcomes, with the latter as a key anchor. A set of evaluation criteria is also suggested.Establishing a definition of disease modification in SLE will clarify which treatments can be considered disease modifying, provide an opportunity to harmonise future clinical trial outcomes and enable comparison between therapies, all of which could ultimately help to improve patient outcomes. This publication seeks to catalyse further discussion and provide a framework to develop an accepted definition of disease modification in SLE.
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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 | 0.145 | 0.097 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".