One hundred and ten years of Allergen Immunotherapy: A journey from empiric observation to evidence
Bibliographic record
Abstract
One hundred and ten years after Noon's first clinical report of the subcutaneous application of allergen extracts, allergen immunotherapy (AIT) has evolved as the most important pillar of the treatment of allergic patients. It is the only disease-modifying treatment option available and the evidence for its clinical efficacy and safety is broad and undisputed. Throughout recent decades, more insights into the underlying mechanisms, in particular the modulation of innate and adaptive immune responses, have been described. AIT is acknowledged by worldwide regulatory authorities, and following the regulatory guidelines for product development, AIT products are subject to a rigorous evaluation before obtaining market authorization. Knowledge and practice are anchored in international guidelines, such as the recently published series of the European Academy of Allergy and Clinical Immunology (EAACI). Innovative approaches continue to be further developed with the focus on clinical improvement by, for example, the usage of adjuvants, peptides, recombinants, modification of allergens, new routes of administration, and the concomitant use of biologicals. In addition, real-life data provide complementary and valuable information on the effectiveness and tolerability of this treatment option in the clinical routine. New mobile health technologies and big-data approaches will improve daily treatment convenience, adherence, and efficacy of AIT. However, the current coronavirus disease 2019 (COVID-19) pandemic has also had some implications for the feasibility and practicability of AIT. Taken together, AIT as the only disease-modifying therapy in allergic diseases has been broadly investigated over the past 110 years laying the path for innovations and further improvement.
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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 | 0.041 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.028 |
| 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".