Evaluation of serum CC-16 as a biomarker for COPD in the ECLIPSE cohort
Bibliographic record
Abstract
BACKGROUND: Circulating levels of Clara cell secretory protein-16 (CC-16) have been linked to Clara cell toxicity. It has therefore been suggested that this protein may be a useful marker of chronic obstructive pulmonary disease (COPD). METHODS: Serum CC-16 levels were measured in 2083 individuals aged 40-75 years with COPD and a smoking history of >or=10 pack-years, 332 controls with a smoking history of >or=10 pack-years and normal lung function and 237 non-smoking controls. RESULTS: Serum CC-16 had a coefficient of repeatability of 2.90 over 3 months in a pilot study of 267 individuals. The median serum CC-16 level was significantly reduced in a replication group of 1888 current and former smokers with COPD compared with 296 current and former smokers without airflow obstruction (4.9 and 5.6 ng/ml, respectively; p<0.001) and 201 non-smokers (6.4 ng/ml; p<0.001). Serum levels of CC-16 were lower in current than in former smokers with GOLD stage II and III COPD but were not different in individuals with stage IV disease. Former, but not current smokers, with COPD had lower serum CC-16 levels with increasing severity of COPD (GOLD II vs GOLD IV COPD: 5.5 and 5.0 ng/ml, p = 0.006; r = 0.11 with forced expiratory volume in 1 s, p<0.001) and had significantly higher levels if they also had reversible airflow obstruction (p = 0.034). Serum CC-16 was affected by gender and age (r = 0.35; p<0.001) in subjects with COPD but not by body mass index or the presence of either chronic bronchitis or emphysema. CONCLUSIONS: Serum CC-16 levels are reduced in individuals with COPD and there is a weak correlation with disease severity in former smokers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".