High Baseline Serum Clara Cell 16 kDa Predicts Subsequent Lung Disease Worsening in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Clara cell secretory protein (CC16) is a sensitive marker of bronchial epithelial cell damage. The CC16 serum level is elevated in patients with pulmonary fibrosis, but its predictive value on lung disease progression has not yet been studied. We aimed to assess the value of serum CC16 concentration in predicting lung disease deterioration in patients with systemic sclerosis (SSc). METHODS: We prospectively analyzed and followed 106 patients with SSc during a 4-year period for the risk of developing combined deleterious event, defined as a 10% decrease in total lung capacity or forced vital capacity from baseline, or death, according to serum CC16 at inclusion. Receiver-operating characteristic (ROC) curve analysis was performed for prediction of events during the first 2 years after inclusion. Cumulative risks of combined events were computed by Kaplan-Meier analysis. RESULTS: The best cutoff level of serum CC16 for prediction of a combined event was 33 ng/ml, with 76% sensitivity and 65% specificity (area under the ROC curve: 0.71, 95% CI 0.61-0.81, p < 0.0001). Progression of lung disease evaluated by a mean time-to-event differed between patients with high baseline serum CC16 (42.8 mos, 36.3-49.3) and those with low serum CC16 (56.3 mos, 50.9-61.7; log-rank test, p < 0.001). After adjustment for age, duration of disease, clinical and lung function measures, the risk of combined event occurrence in patients with high serum CC16 was significantly higher than in those with low CC16 (HR 2.9, 1.2-6.75, p < 0.05). CONCLUSION: High baseline serum CC16 predicts lung disease worsening in patients with SSc.
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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 | 0.003 |
| 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) | 0.002 | 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".