Blood eosinophils in COPD to predict exacerbations and inform inhaled corticosteroid use: Need for further evidence?
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive respiratory illness characterized by breathing difficulty and restricted lung air flow. Its prevalence in 2010 was estimated at 384 million worldwide, with 3 million COPD-related deaths reported in 2015. Although COPD is a gradually developing disease, it is marked by acute episodes, known as exacerbations, which can hospitalize patients and accelerate progression. Identifying susceptible patients and better exacerbation management are, therefore, urgently needed. Although most COPD sputum is infiltrated by neutrophils, a subset is burdened instead by eosinophilia, similar to asthma. Evidence had emerged linking eosinophilia to exacerbations, suggesting that blood or sputum eosinophils may indeed serve as a potential prognostic exacerbation biomarker. With regard to treatment, inhaled long-acting beta agonists and long-acting muscarinic antagonists have been the cornerstone of COPD management. However, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) issued its most recent 2019 statement, advocating and newly incorporating inhaled corticosteroids (ICSs) for patients with blood eosinophils over 300 cells/μL. The majority of the present evidence for using blood eosinophil count to inform ICS usage has arisen from post hoc clinical trial analysis. No prospective studies have provided strong evidence that utilizing a blood eosinophil count of 300 cells/µL benefits COPD patients. In light of the substantial ICS side-effects, the GOLD 2019 recommendation for ICSs may be premature. This review will summarize the evidence to date for a critical overview of the current status of blood eosinophils to predict exacerbations and inform ICS treatment.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".