Defining a severe asthma super-responder: findings from a Delphi process
Bibliographic record
Abstract
Background: Clinicians are increasingly recognising severe asthma patients in whom biologicals and other add-on therapies lead to dramatic improvement. Because there is no agreed upon super-responder (SR) definition at present, we surveyed severe asthma experts using a modified Delphi process in order to define an international consensus-based definition of a severe asthma ‘super-responder’. Methods: The Delphi panel comprised 81 participants (94% specialist pulmonologists or allergists) from 24 countries and consisted of 3 iterative online voting rounds. Consensus on individual items, whether acceptance or rejection, required at least 70% agreement by panel members. Results: Consensus was achieved that the SR definition should be based on improvement across 3 or more domains assessed over 12 months. Major SR criteria included exacerbation elimination, a large improvement in asthma control (≥ 2x the minimal clinically important difference) and cessation of maintenance of oral steroids (or weaning to adrenal insufficiency). Minor SR criteria comprised a 75% exacerbation reduction, having well controlled asthma and a 500mL or greater improvement in FEV1. The SR definition needs to incorporate quality of life measures, though current tools can be difficult to implement in a clinical setting and further research is needed. Conclusions: This international consensus-based definition of severe asthma super responders is an important prerequisite for better understanding super-responder prevalence, predictive factors and the mechanisms involved. Further research is needed to understand the patient perspective and measure quality of life more precisely in super-responders.
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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 | 0.231 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".