P142 The distribution of blood eosinophil count in a copd clinical trials database: comparing the uk with the rest of the world
Bibliographic record
Abstract
Introduction There is accumulating evidence that blood eosinophil count may have predictive value for those individuals with COPD who are more likely to respond to an inhaled corticosteroid in terms of exacerbation reduction and there is evidence that higher blood eosinophil count can also have some predictive value for those at risk of exacerbations. Blood eosinophil counts are known to be raised in a number of conditions including allergies and parasitic or fungal infections. It is therefore possible that the blood eosinophil count would vary between countries and thus influence their predictive value. We have investigated the distribution of blood eosinophil counts in the UK in comparison with blood eosinophil counts worldwide from data in the GSK clinical trials database. Methods In this post-hoc analysis, the following criteria were used to select studies for consistency with analyses conducted to examine the effects of inhaled corticosteroids on outcomes: global, randomised, double-blind, parallel-group clinical trials in COPD of at least 24 weeks’ duration that included any of fluticasone propionate (FP), fluticasone furoate (FF), salmeterol/FP or FF/vilanterol (VI) as a randomised study drug and a non-steroid-containing arm and for which subjects had a pre-randomisation blood sample taken for eosinophils.1,2 Individual subjects’ pre-randomisation eosinophil counts from countries that recruited at least 100 subjects across all trials were pooled to form the global sample (Argentina, Australia, Canada, Chile, Czech Republic, Denmark, Estonia, France, Germany, Greece, Italy, Korea, Lithuania, Mexico, Netherlands, Norway, Peru, Philippines, Poland, Romania, Russia, Slovakia, South Africa, Spain, Sweden, United Kingdom, United States). Individual subjects’ pre-randomisation eosinophil counts for subjects in the UK were pooled to form the UK sample. An empirical cumulative distribution function (CDF) for the UK sample was overlaid on an empirical CDF plot for the global sample. Results The blood eosinophil count in COPD patients included in these trials in the UK is very similar to that worldwide (Figure). Conclusions This suggests that blood eosinophil count could be used in the UK to help predict response to inhaled corticosteroids in COPD. References Pavord. Lancet Respir Med 2016, in press. Pascoe. Lancet Respir Med 2015.
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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 | 0.017 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".