Allergen exposure chambers: harmonizing current concepts and projecting the needs for the future – an <scp>EAACI</scp> Position Paper
Bibliographic record
Abstract
BACKGROUND: Allergen exposure chambers (AECs) are clinical facilities allowing for controlled exposure of subjects to allergens in an enclosed environment. AECs have contributed towards characterizing the pathophysiology of respiratory allergic diseases and the pharmacological properties of new therapies. In addition, they are complementary to and offer some advantages over traditional multicentre field trials for evaluation of novel therapeutics. To date, AEC studies conducted have been monocentric and have followed protocols unique to each centre. Because there are technical differences among AECs, it may be necessary to define parameters to standardize the AECs so that studies may be extrapolated for driving basic immunological research and for marketing authorization purposes by regulatory authorities. METHODS: For this task force initiative of the European Academy of Allergy and Clinical Immunology (EAACI), experts from academia and regulatory agencies met with chamber operators to list technical, clinical and regulatory unmet needs as well as the prerequisites for clinical validation. RESULTS: The latter covered the validation process, standardization of challenges and outcomes, intra- and interchamber variability and reproducibility, in addition to comparability with field trials and specifics of paediatric trials and regulatory issues. CONCLUSION: This EAACI Position Paper aims to harmonize current concepts in AECs and to project unmet needs with the intent to enhance progress towards use of these facilities in determining safety and efficacy of new therapeutics in the future.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".