Airway autoimmunity, asthma exacerbations, and response to biologics
Bibliographic record
Abstract
Biologic therapies in asthma are indicated in severe disease, and they are directed against specific inflammatory modulators that contribute to pathogenesis and severity. Currently approved biologics target T2 cytokines (IgE, IL-5, IL-4/IL-13, and TLSP) and have demonstrated efficacy in clinical outcomes such as exacerbation rate and oral corticosteroid dose reductions, blood and airway eosinophil depletion, and lung function improvement. However, a proportion of these patients may show inadequate responses to biologics, with either initial lack of improvement or clinical and functional worsening after an optimal initial response. Exacerbations while on a biologic may be due to several reasons, including imprecise identification of the dominant effector pathway contributing to severity, additional inflammatory pathways that are not targeted by the biologic, inaccuracies of the biomarker used to guide therapy, inadequate dosing schedules, intercurrent airway infections, anti-drug neutralizing antibodies, and a novel phenomenon of autoimmune responses in the airways interfering with the effectiveness of the monoclonal antibodies. This review, illustrated using case scenarios, describes the underpinnings of airway autoimmune responses in driving exacerbations while patients are being treated with biologics, device a strategy to evaluate such exacerbations, an algorithm to switch between biologics, and perhaps to consider two biologics concurrently.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".