Preventing the development of SLE: identifying risk factors and proposing pathways for clinical care
Bibliographic record
Abstract
Although challenging, developing evidence-based approaches to an early and accurate diagnosis of systemic lupus erythematosus is a key approach to preventing disease and lupus-associated morbidity and mortality. Advances in our understanding of preclinical and incomplete lupus erythematosus have enabled the identification of risk factors that may predict disease and the development of potential strategies aimed at primary prevention. Emerging data support the notion that there is a temporal disease progression from initial asymptomatic autoimmunity (preclinical lupus) through early clinical features of the disease (incomplete lupus erythematosus) to finally becoming fully classifiable systemic lupus erythematosus (complete lupus erythematosus). Here, we review the demographic, clinical, biomarker as well as genetic and environmental features that are reported to increase the risk of disease progression. Based on these risk factors, we propose a clinical care pathway for patients with early disease. We envisage that such a pathway, through early identification of disease, may improve patient outcomes, while reducing health care costs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".