The nature and outcome of infection in systemic lupus erythematosus
Bibliographic record
Abstract
Infection remains a major cause of morbidity and mortality in systemic lupus erythematosus (SLE). To describe the nature and outcomes of infection and determine their associated risk factors in patients with SLE, we performed a nested case-control study at the University of Toronto Lupus Clinic, with prospective follow-up according to a standard protocol since 1970. Cases were SLE patients seen between January 1987 and January 1992 who had documented infections and controls were patients without infection from the same cohort matched for age, gender and time of visit. The type, site and outcome of infection were recorded for each case. A conditional logistic regression analysis was performed to compare factors associated with infection in cases and their controls. Ninety-three patients had 148 infection episodes; the majority were bacterial, but viral, fungal and protozoan organisms were also identified (multiple organisms in seven). Forty-eight patients required hospital admission and three patients died. Steroids at time of infection, as well as use ever, duration and dose, immunosuppressives at time of infection and use ever, active renal disease, CNS damage, SLEDAI at the time of infection, adjusted mean SLEDAI and variability measure were significantly associated with infection by univariate analysis. By multivariate analysis one factor remained statistically significant: use of steroids ever (P = 0.029). Infection carries a large burden for SLE patients. Until new medications which will control disease activity without predisposing to infection are developed, careful titration of steroids and cytotoxic drugs to control disease activity will remain crucial.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".