How to improve morbidity and mortality in systemic lupus erythematosus
Bibliographic record
Abstract
Systemic Lupus Erythematosus/Series Editors: D. Isenberg and C. Gordon SLE is a chronic multisystem disorder of presumed autoimmune origin in which cytotoxic antibodies, or circulating immune complexes, give rise to tissue damage often resulting in end organ disease and even mortality. The first large series to study mortality in this disease in 1955 revealed a survival of less than 50% at 5 yr [1]. Thus, in the past four decades much of the therapeutic initiatives in this disease have been directed at controlling the acute organ attack and inflammatory response in order to minimize tissue damage and to decrease the very high mortality rates. The therapeutic interventions have focused on the use of corticosteroids and cytotoxic agents in appropriate doses to minimize mortality on the one hand and morbidity from the disease or from the treatments on the other. This approach has in fact been quite successful and the overall mortality rates have improved dramatically over this period of time [2, 3] (Table 1). The reported 20‐yr survival of 68% [22] in the 1990s in contrast to the 50% 5‐yr survival in 1955 indicates success in controlling the acute immunological injury typical of this condition. Furthermore, we have shown that this improved survival was greater than the improvement in survival observed in the general population during the same time. The standardized mortality ratios for SLE patients improved from 10.1 in the 1970s, to 4.8 in the early 1980s, and to 3.3 in the early 1990s [27].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".