Novel approaches and perspectives in allergen immunotherapy
Bibliographic record
Abstract
In this review, we report on relevant current topics in allergen immunotherapy (AIT) which were broadly discussed during the first Aarhus Immunotherapy Symposium (Aarhus, Denmark) in December 2015 by leading clinicians, scientists and industry representatives in the field. The aim of this symposium was to highlight AIT-related aspects of public health, clinical efficacy evaluation, mechanisms, development of new biomarkers and an overview of novel therapeutic approaches. Allergy is a public health issue of high socioeconomic relevance, and development of evidence-based action plans to address allergy as a public health issue ought to be on national and regional agendas. The underlying mechanisms are in the focus of current research that lays the ground for innovative therapies. Standardization and harmonization of clinical endpoints in AIT trials as well as current knowledge about potential biomarkers have substantiated proof of effectiveness of this disease-modifying therapeutic option. Novel treatments such as peptide immunotherapy, intralymphatic immunotherapy and use of recombinant allergens herald a new age in which AIT may address treatment of allergy as a public health issue by reaching a large fraction of patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".