Early damage as measured by the SLICC/ACR damage index is a predictor of mortality in systemic lupus erythematosus
Bibliographic record
Abstract
The aim of this study was to determine whether early damage accrued in SLE as measured by the SLICC/ACR Damage Index predicts mortality in an inception cohort of lupus patients that have been followed prospectively in a single centre. SLE patients from the University of Toronto Lupus Clinic presenting within 1 y of their diagnosis prior to 1988 were included. This enabled all patients to be potentially followed for at least 10 y. Yearly SLICC/ACR Damage Index scores were determined for each patient. Early damage was defined as a score > or = 1 and no damage as a score of 0 at the initial assessment. Log rank test was used to compare the survival experience between those with and without damage, with all patients being censored at 10 y. Two-hundred and sixty-three patients were identified in this inception cohort who were followed for 10 y. One-hundred and ninety patients (72%) had a SLICC/ACR Damage Index score of 0 (no damage) while 73 patients (28%) had at least one SLICC/ACR Damage Index item scored (early damage). Twenty-five percent of lupus patients who exhibited damage at their first SLICC/ACR Damage Index assessment died within 10 y of their illness as compared to only 7.3% who had no early damage (log rank P-value = 0.0002). SLE patients who died within 10 y were more likely to have renal damage (P = 0.013), and a trend toward more cardiovascular disease (P = 0.056), compared to patients who were alive. Early damage as reflected by the initial SLICC/ACR Damage Index is associated with a higher rate of mortality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".