Serum Interleukin 6 Is Predictive of Early Functional Decline and Mortality in Interstitial Lung Disease Associated with Systemic Sclerosis
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it