Reply to: Decoding the eosinophil connection: implications for precision treatment of emphysematous in COPD
Bibliographic record
Abstract
Extract We greatly appreciate W. Sun's interest in our recent publication [1]. Eosinophil levels are an important predictive and prognostic biomarker in COPD. Our data demonstrated that airway sampling for eosinophils better represents type 2 inflammation in airway epithelial cells than peripheral blood sampling, and that airway eosinophilia predicts responses to inhaled corticosteroids. Furthermore, patients with airway but not blood eosinophilia demonstrated increased interleukin (IL)-13 activation, along with other biomarkers of type 2 inflammation. Together, these data indicate that airway eosinophilia may be a promising biomarker to predict therapeutic responses to anti-IL-4R biologics that inhibit IL-4 and IL-13 signalling [2]. There is still a pressing need to understand the role of distinct subtypes of eosinophils on disease pathology and progression in COPD [3]. The DISARM study did not differentiate between inflammatory and resident eosinophils of the lower airways, but this distinction may be important in determining the response to anti-IL-5 therapies in COPD [4].
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".