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 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) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".