Criteria to evaluate efficacy of biologics in asthma: a Global Asthma Association survey
Bibliographic record
Abstract
BACKGROUND: Currently, there are no universally accepted criteria to measure the response to biologics available as treatment for severe asthma. This survey aims to establish consensus criteria to use for the evaluation of response to biologics after 4 months of treatment. METHOD: Using Delphi methodology, a questionnaire including 10 items was validated by 13 international experts in asthma. The electronic survey circulated within the Interasma Scientific Network platform. For each item, five answers were proposed graduated from 'no importance' to 'very high importance' and by a score (A = 2 points; B = 4 points; C = 6 points; D = 8 points; E = 10 points). The final criteria were selected if the median score for the item was ≥7 and > 60% of responses according 'high importance' and 'very high importance'. All selected criteria were validated by the experts. RESULTS: Four criteria were identified: reduce daily systemic corticosteroids dose by ≥50%; decrease the number of asthma exacerbations requiring systemic corticosteroids by ≥50%; have no/minimal side effects; and obtain asthma control according validated questionnaires. The consensual decision was that ≥3 criteria define a good response to biologics. CONCLUSIONS: Specific criteria were defined by an international panel of experts and could be used as tool in clinical practice.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".