Relationship between measures of type II inflammation and patient reported cough severity and quality of life outcomes in patients with chronic cough
Bibliographic record
Abstract
Backgroud: The relationship between peripheral blood and sputum eosinophilia and patient reported outcomes (PROs) in patients with chronic cough (CC) remains unclear. Objective: We aimed to evaluate correlations between peripheral blood and sputum eosinophilia with cough PROs including the Leicester Cough Questionnaire (LCQ) and cough severity Visual Analogue Scale (CS-VAS). Methods: We analyzed patients with CC who had their blood and sputum eosinophils, LCQ, and CS-VAS assessed at baseline prior to treatment in two separate studies. Spearman’s rank correlation coefficients were calculated. Results: Seventy participants [mean age (S.D.) 58.0±13.2, 59% female, cough duration 9.6±8.8 yrs] were included in the analysis. At baseline, patients had mean scores of 10.7±3.1 on the LCQ and 62.7 mm (±22.1) on the VAS. Their median (IQR) peripheral blood eosinophils was 0.2 cells/L (0.1-0.25) and % sputum eosinophils was 2.3% (1.1-4.5). We found weak non-significant correlations between peripheral blood and sputum eosinophilia with LCQ and CS-VAS (Figure 1). Conclusion: In patients with CC, type II biomarkers poorly correlated with subjective cough PROs at baseline. Prospective data of changes after treatment should evaluate whether biomarkers of inflammation can predict improvement in cough.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".