How to design and evaluate randomized controlled trials in immunotherapy for allergic rhinitis: an ARIA-GA2LEN statement
Bibliographic record
Abstract
To cite this article: Bousquet J, Schünemann HJ, Bousquet PJ, Bachert C, Canonica GW, Casale TB, Demoly P, Durham S, Carlsen K-H, Malling H-J, Passalacqua G, Simons FER, Anto J, Baena-Cagnani CE, Bergmann K-C, Bieber T, Briggs AH, Brozek J, Calderon MA, Dahl R, Devillier P, Gerth van Wijk R, Howarth P, Larenas D, Papadopoulos NG, Schmid-Grendelmeier P, Zuberbier T. How to design and evaluate randomized controlled trials in immunotherapy for allergic rhinitis: an ARIA-GA2LEN statement. Allergy 2011; 66: 765–774. Abstract Specific immunotherapy (SIT) is one of the treatments for allergic rhinitis. However, for allergists, nonspecialists, regulators, payers, and patients, there remain gaps in understanding the evaluation of randomized controlled trials (RCTs). Although treating the same diseases, RCTs in SIT and pharmacotherapy should be considered separately for several reasons, as developed in this study. These include the severity and persistence of allergic rhinitis in the patients enrolled in the study, the problem of the placebo, allergen exposure (in particular pollen and mite), the analysis and reporting of the study, the level of symptoms of placebo-treated patients, the clinical relevance of the efficacy of SIT, the need for a validated combined symptom–medication score, the differences between children and adults and pharmacoeconomic analyses. This statement reviews issues raised by the interpretation of RCTs in sublingual immunotherapy. It is not possible to directly extrapolate the rules or parameters used in medication RCTs to SIT. It also provides some suggestions for the research that will be needed. Interestingly, some of the research questions can be approached with the available data obtained from large RCTs.
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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 | 0.706 | 0.805 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.034 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".