Tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils): a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Asthma guidelines guide health practitioners to adjust treatments to the minimum level required for asthma control. As many people with asthma have an eosinophilic endotype, tailoring asthma medications based on airway eosinophilic levels (sputum eosinophils or exhaled nitric oxide, FeNO) may improve asthma outcomes. OBJECTIVE: To synthesise the evidence from our updated Cochrane systematic reviews, for tailoring asthma medication based on eosinophilic inflammatory markers (sputum analysis and FeNO) for improving asthma-related outcomes in children and adults. DATA SOURCES: Cochrane reviews with standardised searches up to February 2017. STUDY SELECTION: The Cochrane reviews included randomised controlled comparisons of tailoring asthma medications based on sputum analysis or FeNO compared with controls (primarily clinical symptoms and/or spirometry/peak flow). RESULTS: The 16 included studies of FeNO-based management (seven in adults) and 6 of sputum-based management (five in adults) were clinically heterogeneous. On follow-up, participants randomised to the sputum eosinophils strategy (compared with controls) were significantly less likely to have exacerbations (62 vs 82/100 participants with ≥1 exacerbation; OR 0.36, 95% CI 0.21 to 0.62). For the FeNO strategy, the respective numbers were adults OR 0.60 (95% CI 0.43 to 0.84) and children 0.58 (95% CI 0.45 to 0.75). However, there were no significant group differences for either strategy on daily inhaled corticosteroids dose (at end of study), asthma control or lung function. CONCLUSION: Adjusting treatment based on airway eosinophilic markers reduced the likelihood of asthma exacerbations but had no significant impact on asthma control or lung function.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".