TYPE I INTERFERON STATUS AND CLINICAL MANIFESTATIONS IN A LARGE COHORT OF PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
ABSTRACT Objective: Type I interferon (IFN) plays a key role in SLE pathogenesis, and an elevated IFN gene signature (IGS) has been associated with increased disease severity. This study aimed to retrospectively analyze the clinical and serological characteristics of SLE patients based on IFN-high or IFN-low status. Methods: We analyzed a large cohort of 506 patients with SLE from the University of Toronto Lupus Clinic. Patients were classified as IFN-high or IFN-low based on IGS measured using the DxTerity Modular Immune Profile test. Demographic data, disease activity scores (SLE Disease Activity Index-2000 [SLEDAI-2K], Adjusted Mean SLEDAI-2K [AMS], Adjusted AMS Glucocorticoids [AMSG]), cumulative organ involvement, autoantibody profiles, and medication use were compared between high and IFN-low groups. Results: Of the 506 patients, 291 (57.5%) were IFN-high and 215 (42.5%) were IFN-low. IFN-high patients were younger at study entry (median 46.3 vs. 54.2 years) and had shorter disease duration (median 14.1 vs. 22.7 years). IFN-high patients had higher disease activity scores (SLEDAI-2K, AMS, AMSG) and were more likely to be on glucocorticoids (38.5% vs. 27%) and immunosuppressants (63.6% vs. 45.6%), particularly mycophenolate (39.5% vs. 24.7%). They also had a greater prevalence of positive autoantibodies. Despite higher disease activity, cumulative damage (SDI) was similar between IFN-high and IFN-low groups. Conclusions: Patients with an elevated IGS have more active and severe disease, accumulating more autoantibodies and requiring greater immunosuppression. Retrospective AMS/AMSG analyses further support IGS as a predictor of disease burden. Future studies should explore its role in guiding personalized treatment strategies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".