Biologic therapy practices in severe asthma; outcomes from the <scp>UK</scp> Severe Asthma Registry and survey of specialist opinion
Bibliographic record
Abstract
BACKGROUND: several biological treatments have become available for management of severe asthma. There is a significant overlap in the indication of these treatments with lack of consensus on the first-line biologic choice and switching practice in event of treatment failure. AIMS: to evaluate outcomes of biologic treatments through analysis of the UK Severe Asthma Registry (UKSAR), and survey of the UK severe asthma specialists' opinion. METHODS: patients registered in the UKSAR database and treated with biologics for severe asthma in the period between January 2014 and August 2021, were studied to explore biologic treatments practice. This was complemented by survey of opinion of severe asthma specialists. RESULTS: ). Biologics use included mepolizumab 1,115 (44.8%), benralizumab 925 (37.1%), omalizumab 432 (17.3%), dupilumab 13 (0.5%), and reslizumab 5 (0.2%). Patients on omalizumab were younger and had earlier age of onset asthma than those prescribed mepolizumab or benralizumab. Patients prescribed mepolizumab and benralizumab had similar clinical characteristics. Those on benralizumab were more likely to continue treatment at approximately one year follow up (93.9%), than those on mepolizumab (80%), or omalizumab (69.6%). The first choice biologic differed between centres and changed over the study time period. Experts' opinion also diverged in terms of biologic initiation choice and switching practice. CONCLUSION: We observed significant variation and divergence in the prescribing practices of biologics in severe asthma that necessitates further research and standardisation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".