Blood eosinophils in COPD to inform inhaled corticosteroid use: Ready to be used in clinical practice
Bibliographic record
Abstract
The GOLD report and the Canadian Thoracic Society Clinical Practice Guideline on pharmacotherapy in patients with COPD have incorporated blood eosinophils as a potential biomarker for treatment use in COPD patients.The first part of this review highlights situation where blood eosinophil is not useful. The second part reviews clinical trials (randomized control trial, withdrawal design) and an observational study (clinical database) in COPD patients with a history of exacerbations showing that inhaled corticosteroid-containing regimens reduces exacerbation rates in patients according to their level of blood eosinophils. This relationship between blood eosinophils and treatment response has been shown to follow a continuum. However, to be more practical, levels of blood eosinophils have been proposed for the clinician. The current evidence indicates that assessment of patient’s exacerbation risk and blood eosinophil count ⩾300 cells·μL− predict a high likelihood of clinical benefit with inhaled corticosteroid-containing regimens, and no or minimal benefit with eosinophils < 100-150 cells·μL−.The review concludes based on evidence that we must take action and stop waiting. Blood eosinophil level is a good predictive biomarker; it can discriminate which COPD patients are likely or not to respond to a specific therapy.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".