Disease-modifying therapies in systemic lupus erythematosus for extrarenal manifestations
Bibliographic record
Abstract
Our 2022 published working definition of disease modification in systemic lupus erythematosus (SLE) was 'minimising disease activity with the fewest treatment-associated toxicities and slowing or preventing organ damage progression'. The objective of this review was to classify current SLE treatments according to the proposed non-renal disease modification criteria excluding toxicities. Based on a review of select clinical trial (n=32) and observational study (n=54) publications for 14 SLE medications across different therapeutic classes, and the authors' clinical experience, we evaluated disease modification potential as per the proposed framework at three time points. Specific criteria used to determine disease modification potential included a drug's capacity to reduce: (1) non-renal disease activity, (2) severe flares, (3) use of steroids/immunosuppressants and (4) organ damage accrual. Criteria 1-3 were assessed at 1 year and 2-5 years and, when positive, were considered evidence for disease modification potential; criterion 4 was used to confirm disease modification at >5 years. Each treatment received one of four mutually exclusive designations at each time point: (a) criterion met, (b) indications of criterion met despite insufficient evidence in the literature, (c) inconclusive and (d) no available supportive data. This review excludes an assessment of potential toxicities. Eight of the 14 SLE treatments met ≥1 disease modification criteria up to year 5. Hydroxychloroquine improved overall survival at >5 years, suggesting long-term disease modification, but no data on specific organ systems were reported. Belimumab was the only treatment to meet all criteria. Belimumab and hydroxychloroquine met disease modification definitions across three time points. Evidence for other SLE therapies was incomplete, particularly at >5 years. Future studies are warranted for other treatments to meet the disease modification criteria. We discuss challenges to classification and possible updates to our published criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".