Perspectives in allergen immunotherapy: 2017 and beyond
Bibliographic record
Abstract
The Future of the Allergists and Specific Immunotherapy (FASIT) workshop provides a regular platform for global experts from academia, allergy clinics, regulatory authorities and industry to review developments in the field of allergen immunotherapy (AIT). The most recent meeting, held in February 2017, had two main themes: advances in AIT and hot topics in AIT from the regulatory point of view. The first theme covered opportunities for personalized AIT, advances in adjuvants and delivery systems, and the development of new molecules and future vaccines for AIT. Key topics in the second part of the meeting were the effects of the enactment of European Directive 2001/83 on the availability of allergens for therapy and diagnosis across the EU, the challenges of conducting Phase 3 studies in the field, the future role of allergen exposure chambers in AIT studies and specific considerations in performing AIT studies in the paediatric population. Finally, the group highlighted the forthcoming EAACI guidelines and their particular importance for the standardization of practice in the treatment of allergies. This review presents a comprehensive insight into those panel discussions and highlights unmet needs and also possible solutions to them 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".