Clinical outcome of mild to very-mild asthma with high sputum eosinophils: a prospective longitudinal study.
Bibliographic record
Abstract
Background: Only a small proportion of patients with very mild to mild asthma will develop a more severe form of the disease. High eosinophils appear to be predictors of poorer short-term outcomes in mild asthma, but data are needed on patients who experience an increase in disease severity. Aim: To compare evolution of very mild and mild asthmatics showing high (≥3% S-EOS) vs low (<3% S-EOS) baseline sputum eosinophils. Methods: This longitudinal multicenter study included 2 visits >12 months apart. Adults with very mild or mild asthma and a previous sputum differential cell count result identified through local databases were asked to come back for a follow-up visit. Annual decline in forced expiratory volume in one second (FEV1) and change in asthma medication between visits were compared between groups. Results: We included 66 subjects (41 females (64%), mean age 34 years) of whom 26 (39%) had ≥3% S-EOS. The mean time between visits was 10.3 years. Groups were similar for demographics and clinical data, except for a lower PC20 in the ≥3% S-EOS group (1.65 (0.72-2.58) mg/mL vs 3.85 (2.37-5.30) mg/mL, P=0.02). Although decline in FEV1 was similar (≥3% S-EOS: -0.52 (−2.45-1.41) mL/year vs <3% S-EOS: -0.46 (−1.94-1.01) mL/year, P=0.63), more subjects in the ≥3% S-EOS group tended to show such decline (77% vs 50%, P=0.08). Moreover, a higher proportion of subjects with ≥3% S-EOS evolved to a more severe disease based on ICS doses requirement (46% vs 14%, P=0.03). Conclusions: Using available clinical characteristics, clinicians still face challenges to identify who will evolve to a more severe form of asthma. Use of S-EOS could help identify patients at risk of poorer evolution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".