Circulating surfactant protein D as a potential lung-specific biomarker of health outcomes in COPD: a pilot study
Bibliographic record
Abstract
BACKGROUND: There is a paucity of surrogate lung-specific biological markers that can be used to track disease progression and predict clinical outcomes in chronic obstructive pulmonary disease (COPD). The principal aim of this pilot study was to determine whether circulating surfactant protein D (SPD) or Clara Cell protein-16 (CC16) levels are associated with lung function or health status in patients with severe COPD. METHODS: We studied 23 patients with advanced COPD. Lung function measurements, Chronic Respiratory Disease Questionnaire (CRQ) scores, and serum levels of SPD, CC16, and C-reactive protein (CRP) were determined at baseline and at 3 months. RESULTS: At baseline, FEV(1) was inversely associated with serum SPD levels (P = 0.045) but not with CC16 (P = 0.675) or CRP levels (P = 0.549). Over a 3 month period, changes in SPD levels correlated significantly with changes in CRQ scores (adjusted P = 0.008) such that patients who had the largest declines in serum SPD levels experienced the largest gains in health status. The association was particularly notable between circulating SPD level and the dyspnea domain of the CRQ score (P = 0.018). Changes in CC16 or CRP levels did not correlate with changes in CRQ scores. CONCLUSION: Changes in serum SPD levels tracked well with changes in health status over a 3 month period in patients with severe COPD. These data suggest that circulating SPD levels may be useful biomarkers to track health outcomes of COPD patients.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".