Circulating surfactant protein-D and the risk of cardiovascular morbidity and mortality
Bibliographic record
Abstract
AIMS: Surfactant protein-D (SP-D) is a lung-specific protein that is detectable in human plasma. We determined the relationship of circulating SP-D to cardiovascular disease (CVD) and total mortality in subjects with and without CVD. METHODS AND RESULTS: Plasma SP-D levels were measured in 806 patients who underwent coronary angiography to assess its predictive value for cardiovascular mortality. Serum SP-D levels were also measured in a replication cohort to assess its relationship with CVD events in 4468 ex- and current smokers without a known history of coronary artery disease (CAD). Patients who died during follow-up had significantly higher plasma SP-D levels than those who survived (median 85.4 vs. 64.8 ng/mL; P < 0.0001). Those in the highest quintile of SP-D had 4.4-fold higher risk of CVD mortality than those in the lowest quintile (P < 0.0001) independent of age, sex, and plasma lipid levels. In a group of current and ex-smokers without a known history of CAD, serum SP-D levels were elevated in those who died or were hospitalized for CVD compared with those who did not (median 99.8 vs. 90.6 ng/mL; P = 0.0001). CONCLUSION: Circulating SP-D is a good predictor of cardiovascular morbidity and mortality and adds prognostic information to well-established risk factors such as age, sex, and plasma lipids and is a promising biomarker to link lung inflammation/injury to CVD.
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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) | 0.000 | 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) | 0.001 | 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".