24 A molecular signature based on ifn gene signature and serology defines two populations of patients with different baseline disease activity
Bibliographic record
Abstract
Background and Aims Randomised controlled trials in SLE have shown that response to treatment is influenced by baseline disease activity. The current investigation used objective molecular and biochemical baseline parameters to characterise SLE patients in two large multinational trials (n=2262 patients). Methods Patients were categorised with four dichotomous baseline parameters. SLE(+) was defined by any of the following: IFN signature (high), anti-dsDNA (+), C3 (low) and/or C4 (low). SLE(-) required all of the following: IFN signature (normal), anti-dsDNA (-), C3 (normal) and C4 (normal). Results Baseline RNA transcript data were available for 1747 of 2262 patients. When IFN status was combined with the serology criteria, 1500 (86%) were classified as SLE(+) and 247 (14%) were classified as SLE(-). At baseline, SLE(-) patients had significantly lower mean SLEDAI scores (8.3) compared to SLE(+) (10.7). Baseline SLEDAI <10 was observed in 72% of SLE(-) compared to 38% of SLE(+). The proportion on corticosteroids at baseline was 49% in SLE(-) compared to 78% in SLE(+); the proportion on immunosuppressants at baseline was 31% in SLE(-) compared to 44% in SLE(+). In the US, 22% were SLE(-) compared to 10% for Latin America, 7% for Europe, and 5% for ROW. Conclusions A subset of clinical trial patients was identified using biochemical and molecular markers with high sensitivity for SLE. Seronegative SLE patients with normal IFN gene signature had lower disease activity and were taking less background medication at baseline, two factors which have been negatively associated with response to treatment in some previous trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".