Anti-dsDNA and anti-Sm antibodies do not predict damage in systemic lupus erythematosus
Bibliographic record
Abstract
We aimed to determine whether anti-dsDNA and anti-Sm antibodies predict damage in systemic lupus erythematosus (SLE). Five-hundred inception patients from the University of Toronto Lupus Clinic were studied. Predictors assessed for the entire study period were: (1) raised anti-dsDNA on two consecutive occasions; (2) anti-dsDNA levels (normal, mildly or highly elevated); (3) presence of antiSm on any occasion. To account for disease duration, the following were assessed at three years post-inception: raised anti-dsDNA on two consecutive occasions; anti-dsDNA levels. These predictors were correlated with the following outcomes: (1) overall SLICC/ACR Damage Index (SDI) at the end of the study period; (2) frequency of damage in the cardiovascular, neuropsychiatric, musculoskeletal and renal components of SDI; ((3) SDI at five years for the predictors assessed at three years post-inception. In the multivariate analysis, presence of anti-DNA antibodies or of anti-SM were non-significant but sex, age at SLE diagnosis, disease duration, corticosteroid use and cumulative dose were strong predictors of damage. Raised anti-dsDNA on two occasions or anti-dsDNA levels in the three years post-inception patients did not predict damage at five years. The presence and levels of anti-dsDNA and anti-Sm antibodies do not predict damage in SLE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".