Multi analyte profiling and variability of inflammatory markers in blood and induced sputum in patients with stable COPD
Bibliographic record
Abstract
BACKGROUND: We analyzed serial concentrations of multiple inflammatory mediators from serum and induced sputum obtained from patients with stable COPD and controls. The objective was to determine which proteins could be used as reliable biomarkers to assess COPD disease state and severity. METHODS: Forty-two subjects; 21 with stable COPD and 21 controls, were studied every 2 weeks over a 6-week period. Serum and induced sputum were obtained at each of 3 visits and concentrations of 19 serum and 22 sputum proteins were serially assessed using multiplex immunoassays. We used linear mixed effects models to test the distribution of proteins for an association with COPD and disease severity. Measures of within- and between-subject coefficients of variation were calculated for each of the proteins to assess reliability of measurement. RESULTS: There was significant variability in concentrations of all inflammatory proteins over time, and variability was greater for sputum proteins (median intra-subject coefficient of variation 0.58) compared to proteins measured in serum (median intra-subject coefficient of variation 0.32, P = 0.03). Of 19 serum proteins and 22 sputum proteins tested, only serum CRP, myeloperoxidase and VEGF and sputum IL-6, IL-8, TIMP-1, and VEGF showed acceptable intra and inter-patient reliability and were significantly associated with COPD, the severity of lung function impairment, and dyspnea. CONCLUSIONS: Levels of many serum and sputum biomarkers cannot be reliably ascertained based on single measurements. Multiple measurements over time can give a more reliable and precise estimate of the inflammatory burden in clinically stable 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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".