Future research trends in understanding the mechanisms underlying allergic diseases for improved patient care
Bibliographic record
Abstract
The specialties of allergy and clinical immunology have entered the era of precision medicine with the stratification of diseases into distinct disease subsets, specific diagnoses, and targeted treatment options, including biologicals and small molecules. This article reviews recent developments in research and patient care and future trends in the discipline. The section on basic mechanisms of allergic diseases summarizes the current status and defines research needs in structural biology, type 2 inflammation, immune tolerance, neuroimmune mechanisms, role of the microbiome and diet, environmental factors, and respiratory viral infections. In the section on diagnostic challenges, clinical trials, precision medicine and immune monitoring of allergic diseases, asthma, allergic and nonallergic rhinitis, and new approaches to the diagnosis and treatment of drug hypersensitivity reactions are discussed in further detail. In the third section, unmet needs and future research areas for the treatment of allergic diseases are highlighted with topics on food allergy, biologics, small molecules, and novel therapeutic concepts in allergen-specific immunotherapy for airway disease. Unknowns and future research needs are discussed at the end of each subsection.
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 | 0.003 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".