Serum IFNα2 levels are associated with disease activity and outperform IFN-I gene signature in a longitudinal childhood-onset SLE cohort
Bibliographic record
Abstract
OBJECTIVE: To study the association of serum IFNα2 levels measured by ultrasensitive single-molecule array (Simoa) and the IFN-I gene signature (IGS) with disease activity and determine whether these assays can mark disease activity states in a longitudinal cohort of childhood-onset SLE (cSLE) patients. METHODS: Serum IFNα2 levels were measured in 338 samples from 48 cSLE patients and 67 healthy controls using an IFNα Simoa assay. Five-gene IGS was measured by RT-PCR in paired whole blood samples. Disease activity was measured by clinical SELENA-SLEDAI and BILAG-2004. Low disease activity was defined by Low Lupus Disease Activity State (LLDAS) and flares were characterized by SELENA-SLEDAI flare index. Analysis was performed using linear mixed models. RESULTS: A clear positive correlation was present between serum IFNα2 levels and the IGS (r = 0.78, P < 0.0001). Serum IFNα2 levels and IGS showed the same significant negative trend in the first 3 years after diagnosis. In this timeframe, mean baseline serum IFNα2 levels decreased by 55.1% (Δ 201 fg/ml, P < 0.001) to a mean value of 164 fg/ml, which was below the calculated threshold of 219.4 fg/ml that discriminated between patients and healthy controls. In the linear mixed model, serum IFNα2 levels were significantly associated with both cSELENA-SLEDAI and BILAG-2004, while the IGS did not show this association. Both IFN-I assays were able to characterize LLDAS and disease flare in receiver operating characteristic analysis. CONCLUSIONS: Serum IFNα2 levels measured by Simoa technology are associated with disease activity scores and characterize disease activity states in cSLE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".