Perspectives in allergen immunotherapy: 2019 and beyond
Bibliographic record
Abstract
The seventh "Future of the Allergists and Specific Immunotherapy (FASIT)" workshop held in 2019 provided a platform for global experts from academia, allergy clinics, regulatory authorities and industry to review current developments in the field of allergen immunotherapy (AIT). Key domains of the meeting included the following: (a) Biomarkers for AIT and allergic asthma; (b) visions for the future of AIT; (c) progress and data for AIT in asthma and the updates of GINA and EAACI Asthma Guidelines (separated for house dust mite SCIT, SLIT tablets and SLIT drops; patient populations) including a review of clinically relevant endpoints in AIT studies in asthma; (d) regulatory prerequisites such as the "Therapy Allergen Ordinance" in Germany; (e) optimization of trial design in AIT clinical research; (f) challenges planning and conducting phase III (field) studies and the future role of Allergen Exposure Chambers (AEC) in AIT product development from the regulatory point of view. We report a summary of panel discussions of all six domains and highlight unmet needs and possible solutions for the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".