Bibliographic record
Abstract
BACKGROUND: Patients with severe refractory asthma represent a small subset of the asthmatic population (between 5% and 10% of all patients) but are the greatest burden to the health care system. New treatment approaches developed to manage some of the phenotypes of severe refractory asthma have included humanized monoclonal antibodies (hMabs). OBJECTIVE: To review the evidence and ascertain whether hMabs provide clinical benefit to patients with severe refractory asthma. METHODS: Studies that examined the efficacy of hMabs against immunoglobulin (Ig) E, tumour necrosis factor-alpha, interleukin (IL)-5, and IL-4⁄IL-13 in patients with severe refractory asthma were reviewed and summarized. RESULTS: Treatment with anti-IgE improved asthma control and reduced severe exacerbations in patients with severe asthma and elevated serum IgE levels. Treatments with hMabs that block tumour necrosis factor-alpha are unlikely to be useful in asthma treatment. In contrast, hMabs that block IL-5 have consistently shown benefit in reducing severe exacerbations in patients with severe refractory asthma with persistent eosinophilia. Finally, hMabs that block IL-13 may provide benefit in patients with elevated blood periostin levels. DISCUSSION: hMabs that block IgE are approved for the treatment of allergic asthma. It is likely that blocking IL-5 will also provide benefit in patients with severe asthma with persistent eosinophilia. These studies have emphasized the importance of careful phenotyping of patients with severe refractory asthma before embarking on treatment with hMabs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".