Prediction of short term mortality in systemic lupus erythematosus with time dependent measures of disease activity.
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of short term mortality in systemic lupus erythematosus (SLE) in terms of time dependent clinical indicators of disease activity from the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI). METHODS: We studied data collected on patients followed at the University of Toronto Lupus Clinic. Clinical and laboratory indicators of disease activity are recorded at each clinic visit and a SLEDAI summary score is calculated. Survival analyses were conducted in which the prognostic value of the time dependent indicators of disease activity was examined on 6-month mortality through a multivariate Cox regression model. Relative risks, confidence intervals, and significance levels were obtained for each indicator to reflect their clinical importance and statistical significance. RESULTS: The sample consisted of 806 patients followed for a median of 6.6 years; 702 (87%) were female, 671 (83%) were Caucasian, and the mean age at first clinic visit was 36 years. Seventy-two patients died within 6 months of their last clinic visit. In a univariate regression model, a categorical variable reflecting total SLEDAI score was highly prognostic for mortality (p < 0.001) and yielded increasing relative risks of 1.28 for SLEDAI 1-5 vs 0, 2.34 for SLEDAI 6-10 vs 0, 4.74 for SLEDAI 11-19 vs 0, and 14.11 for SLEDAI > 20 vs 0. In a separate multivariate Cox model examining the individual components of SLEDAI, presence of organic brain syndrome, retinal changes, cranial nerve involvement, proteinuria, pyuria, pleurisy, fever, thrombocytopenia, and leukopenia each significantly increased the risk of death, while new rash and anti-DNA antibodies conferred protective effects. CONCLUSION: This time dependent Cox regression analysis identified the extent to which SLE disease activity, revealed by SLEDAI, is prognostic for short term mortality. Further, important individual components were identified and their prognostic value for death was estimated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".