Elevated Serum Krebs von den Lungen-6 in Early Disease Predicts Subsequent Deterioration of Pulmonary Function in Patients with Systemic Sclerosis and Interstitial Lung Disease
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of poor prognosis in patients with systemic sclerosis (SSc) associated with interstitial lung disease (ILD). METHODS: Fifty patients with early-stage SSc-ILD who had never received disease-modifying drugs and were either observed for ≥ 10 years or died from ILD-related causes were enrolled. The baseline variables of patients who developed endstage lung disease (ESLD) were compared with those of patients who remained ESLD-free, and the Cox proportional hazard model was used to identify initial factors that correlated with ESLD development. RESULTS: Sixteen patients (32%) developed ESLD during 173.5 ± 64.7 months of followup. Elevated serum Krebs von den Lungen-6 (KL-6) at initial assessment was highly correlated with ESLD development (p = 0.0002). Receiver-operating characteristic curve analysis revealed that a KL-6 value of 1273 U/ml effectively discriminated patients who developed ESLD from those who did not. Patients with KL-6 > 1273 U/ml were less likely to remain ESLD-free compared with those with lower KL-6 levels (p < 0.0001). Multivariate analysis showed that KL-6 > 1273 U/ml was the most reliable predictor of ESLD development (OR 51.2, 95% CI 7.6-343, p < 0.0001). Finally, the initial KL-6 level correlated with the forced vital capacity (FVC) decline rate (r = 0.58, p < 0.0001). CONCLUSION: The natural course of SSc-ILD is highly variable. Baseline serum KL-6 is a biomarker potentially useful for predicting FVC decline.
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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 | 0.001 | 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".