Bibliographic record
Abstract
Alex Mulvanny,1 Augusta Beech,1,2 Jian Li,2 Simon Lea,2 Dave Singh1,2 1Bioanalytical Department, Medicines Evaluation Unit, Manchester, UK; 2Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UKCorrespondence: Alex Mulvanny, Bioanalytical Department, Medicines Evaluation Unit, Manchester, UK, Tel +44 161 946 4066, Email amulvanny@meu.org.ukIntroduction: Sputum biomarker measurements are used to measure airway inflammation in COPD patients. We have previously validated a Luminex assay able to quantify 15 analytes in COPD sputum supernatant. This assay demonstrated sputum protein expression profiles associated with neutrophilia, airway bacterial colonisation and current smoking in COPD.Methods: We report repeat sputum supernatant analysis at 6 months from a sub-group of COPD patients who participated in the original study. 48 COPD patients provided a repeat sputum sample at 6 months. 15 panel analytes were detectable.Results: Repeated sputum cytokine measurements showed a significant positive correlation between baseline and 6 months for all analytes except IL-1RA with intraclass correlation coefficient (ICC) indicating good to excellent repeatability. IL-1β, IL-2, IL-8, IL-17A, G-CSF, MIP-1α, MIP-1β and TNF-α were significantly correlated with sputum neutrophil percentage at 6 months. IL-1β, IL-4, IL-8 and G-CSF were significantly increased in Haemophilus influenzae colonised patients and IL-1β, IL-4, IL-8, G-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β and TNF-α were significantly higher in ex-smoking COPD patients.Discussion: A multiplex immunoassay used at repeated visits in COPD patients showed a high degree of reproducibility for the majority of analytes. There were reproducible inflammatory signatures in sputum associated with clinical characteristics in COPD patients.Keywords: biomarker, validation, luminex, inflammation, respiratory
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".