KL-6 But Not CCL-18 Is a Predictor of Early Progression in Systemic Sclerosis-related Interstitial Lung Disease
Bibliographic record
Abstract
OBJECTIVE: The 2 pneumoproteins, KL-6 and CCL-18, are promising biomarkers in systemic sclerosis (SSc)-related interstitial lung disease (ILD). Our goal was to determine their predictive significance for forced vital capacity % (FVC%) decline within the first year of followup in patients with early SSc-ILD. METHODS: Early SSc patients with imaging-verified ILD enrolled in the Genetics versus Environment in Scleroderma Outcome Study (GENISOS) cohort were included. Annualized rate of change in FVC% based on the baseline and followup measurement within 12-18 months was used as the surrogate outcomes for ILD progression. RESULTS: Eighty-two early SSc-ILD patients with mean disease duration of 2.3 years were investigated. FVC% change ranged from -23% to 38%. Baseline KL-6 levels were higher in patients than healthy controls (p < 0.0001). Higher KL-6 levels were predictive of faster FVC% decline at the 1-year followup (r = -0.23, p = 0.037). Upon categorizing KL-6 using a previously published cutoff of 1273 U/ml, its predictive significance remained in the univariable model (b = -0.07, p = 0.01), indicating that patients with positive KL-6 had on average 7% more decline in annualized percent change of FVC%. Moreover, KL-6 remained an independent predictor after adjustment for sex, disease type, anti-Scl-70, and immunosuppressive treatment status in multivariable models. Although CCL-18 was higher in patients than controls (p < 0.001), its levels did not predict FVC decline rate (p = 0.458). CONCLUSION: KL-6 but not CCL-18 is predictive of early SSc-ILD progression. KL-6 is a promising pneumoprotein that can contribute to SSc-ILD clinical trial enrichment.
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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".