Bibliographic record
Abstract
BACKGROUND: Patients with severe refractory asthma have not achieved asthma control, even with high doses of ICS, usually in combination with LABAs and other maintenance treatments. OBJECTIVE: The most promising approaches currently under investigation are those which reduce airway eosinophils in patients with severe refractory asthma and a persisting airway eosinophilia. RESULTS: Monoclonal antibodies against IL-5 have been shown to improve lung function, improve asthma control, reduce exacerbation risk and allow reduction or elimination of maintenance oral corticosteroids in this subset of patients. Bronchial thermoplasty may provide benefit in improving control and reducing exacerbations in selected patients. The addition of the muscarinic antagonist, tiotropium also improves airflow obstruction, but its benefit on exacerbation risk is not yet established. Other developments being evaluated in severe refractory asthma are CXCR2 antagonists in patients with a persisting neutrophilic airway inflammation, and CRTh2 antagonists, both of which are small molecule antagonists, and hMabs against IL4 and IL-13. Finally, other approaches to reduce receptor numbers, using inhaled anti-sense, has shown to reduce allergen-induced airway eosinophilia, and combining different anti-sense against different targets may become a feasible treatment option. CONCLUSIONS: A variety of new treatment options are being investigated to help improve overall asthma control in patients with severe refractory asthma. These include medications to optimize lung function; bronchial thermoplasty to reduce airway smooth muscle in central airways; and those which target specific inflammatory cells or receptors of inflammatory mediators. CLINICAL RELEVANCE: Patients with severe refractory asthma have the greatest unmet treatment needs to improve asthma control and reduce exacerbation risk. New treatment approaches have been identified which will benefit subsets of these patients. Phenotyping patients is necessary to select those likely to benefit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".