Drug therapies in severe asthma – the era of stratified medicine
Bibliographic record
Abstract
Difficult-to-treat asthma affects up to 20% of patients with asthma and is associated with significant healthcare cost. It is an umbrella term that defines a heterogeneous clinical problem including incorrect diagnosis, comorbid conditions and treatment non-adherence; when these are effectively addressed, good symptom control is frequently achieved. However, in 3-5% of adults with difficult-to-treat asthma, the problem is severe disease that is unresponsive to currently available treatments. Current treatment guidelines advise the 'stepwise' increase of corticosteroids, but it is now recognised that many aspects of asthma are not corticosteroid responsive, and that this 'one size fits all' approach does not deliver clinical benefit in many patients and can also lead to side effects. The future of management of severe asthma will involve optimisation with currently available treatments, particularly corticosteroids, including addressing non-adherence and defining an 'optimised' corticosteroid dose, allied with the use of 'add-on' target-specific novel treatments. This review examines the current status of novel treatments and research efforts to identify novel targets in the era of stratified medicines in severe asthma.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".