Plasma pro-surfactant protein B and lung function decline in smokers
Bibliographic record
Abstract
Plasma pro-surfactant protein B (pro-SFTPB) levels have recently been shown to predict the development of lung cancer in current and ex-smokers, but the ability of pro-SFTPB to predict measures of chronic obstructive pulmonary disease (COPD) severity is unknown. We evaluated the performance characteristics of pro-SFTPB as a biomarker of lung function decline in a population of current and ex-smokers. Plasma pro-SFTPB levels were measured in 2503 current and ex-smokers enrolled in the Pan-Canadian Early Detection of Lung Cancer Study. Linear regression was performed to determine the relationship of pro-SFTPB levels to changes in forced expiratory volume in 1 s (FEV1) over a 2-year period as well as to baseline FEV1 and the burden of emphysema observed in computed tomography (CT) scans. Plasma pro-SFTPB levels were inversely related to both FEV1 % predicted (p=0.024) and FEV1/forced vital capacity (FVC) (p<0.001), and were positively related to the burden of emphysema on CT scans (p<0.001). Higher plasma pro-SFTPB levels were also associated with a more rapid decline in FEV1 at 1 year (p=0.024) and over 2 years of follow-up (p=0.004). Higher plasma pro-SFTPB levels are associated with increased severity of airflow limitation and accelerated decline in lung function. Pro-SFTPB is a promising biomarker for COPD severity and progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".