Serum surfactant protein D is steroid sensitive and associated with exacerbations of COPD
Bibliographic record
Abstract
Surfactant protein (SP)-D is a lung-derived protein that has been proposed as a biomarker for inflammatory lung disease. Serum SP-D was evaluated as a biomarker for components of chronic obstructive pulmonary disease (COPD) in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) cohort and its response assessed to the administration of the anti-inflammatory agent prednisolone. The median level of serum SP-D was significantly elevated in 1,888 individuals with COPD compared to 296 current and former smokers without airflow obstruction (121.1 and 114.3 ng x mL(-1), respectively; p = 0.021) and 201 nonsmokers (82.2 ng x ml(-1); p<0.001). There was no correlation with the severity of COPD. Individuals with COPD who had a serum SP-D concentration that was greater than the 95th percentile of nonsmokers (175.4 ng x mL(-1)) showed an increased risk of exacerbations over the following 12 months (adjusted OR 1.30; 95% CI 1.03-1.63). Treatment with 20 mg x day(-1) prednisolone for 4 weeks resulted in a fall in serum SP-D levels (126.0 to 82.1 ng x mL(-1); p<0.001) but no significant change in post-bronchodilator forced expiratory volume in 1 s. Serum SP-D concentration is raised in smokers and may be useful in identifying individuals who are at increased risk of exacerbations of COPD. It may represent an intermediate measure for the development of novel anti-inflammatory agents.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".