Serum Interleukin 6 Is Predictive of Early Functional Decline and Mortality in Interstitial Lung Disease Associated with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Biomarkers of progression of interstitial lung disease (ILD) are needed to allow early therapeutic intervention in patients with scleroderma-associated disease (SSc-ILD). METHODS: A panel of 8 serum cytokines [interleukin 6 (IL-6), IL-8, IL-10, CCL2, CXCL10, vascular endothelial growth factor, fibroblast growth factor 2, and CX3CL1] was assessed by Luminex bead technology in exploratory cohorts of 74 patients with SSc and 58 patients with idiopathic pulmonary fibrosis (IPF). Mortality and significant lung function decline [forced vital capacity (FVC) ≥ 10%; DLCO ≥ 15%] from date of serum collection were evaluated by proportional hazards analysis. Based on these findings, the prognostic value of serum IL-6, evaluated by ELISA, was assessed in a larger test cohort of 212 patients with SSc-ILD. RESULTS: In the exploratory cohort, only serum IL-6 was an independent predictor of DLCO decline in both IPF and SSc-ILD. The IL-6 threshold level most predictive of DLCO decline within a year was 7.67 pg/ml. In the larger test cohort, serum IL-6 > 7.67 pg/ml was predictive of decline in FVC (HR 2.58 ± 0.98, p = 0.01) and in DLCO (HR 3.2 ± 1.7, p = 0.02) within the first year, and predictive of death within the first 30 months (HR 2.69 ± 0.96, p = 0.005). When stratified according to severity (FVC < 70%), serum IL-6 > 7.67 pg/ml was predictive of functional decline or death within the first year in patients with milder disease (OR 3.1, 95% CI 1.4-7.2, p = 0.007), but not in those with severe ILD. CONCLUSION: In SSc-ILD, serum IL-6 levels appear to be predictive of early disease progression in patients with mild ILD, and could be used to target treatment in this group, if confirmed by prospective studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".