Bibliographic record
Abstract
The eosinophil, whether in the blood or sputum, has been receiving increasing attention as a measure of treatment response or failure in patients with chronic obstructive pulmonary disease (COPD). A recent review by Bafadhel et al. [1] summarises the biology of the eosinophil, its role in airway disease, as well as the clinical data supporting the use of blood eosinophils as a biomarker in airway disease, and more specifically, COPD. To paraphrase their conclusions: while the role of eosinophils in COPD needs clarification, there is now the realisation that blood eosinophils may be a useful guide to therapy. There is, however, substantial within-person variation in blood eosinophil counts [2]. Despite this, there is a significant proportion of patients with COPD in whom blood eosinophil counts are consistently high or low: 65% consistently above or below 0.40×109 per L over a 1-year period in the Leicester cohort [1] and 51% at the 2% cut-off over a 3-year period in the ECLIPSE study [3]. Consistency also appears better when using absolute counts and at lower counts. For example, using a cut-off value of <0.15×109 per L, Southworth et al. [4] found 87% to be in the same category at 6 months and a similar proportion at >2 years of follow-up. It has further been suggested that higher blood eosinophil counts are predictive of future eosinophilic exacerbations [5] and this is supported by the better response to oral corticosteroids in patients with exacerbations characterised by higher blood eosinophils at presentation [1]. The broader importance of the eosinophil in the natural history of airway disease at an early stage is suggested by the predictive value of blood eosinophil counts on decline in forced expiratory volume in 1 s (FEV1) and FEV1/forced vital capacity in young adults from the New Zealand birth cohort, an effect that is independent of asthma and cigarette smoking [6]. There is an increase in pneumonia risk in COPD patients who have higher blood eosinophil counts at baseline
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".