Bibliographic record
Abstract
Specific allergen immunotherapy is clinically effective and disease modifying. It has a duration of effect that exceeds the treatment period and prevents both the progression of allergic rhinitis to asthma and the acquisition of new allergic sensitizations. However, immunotherapy is associated with a high frequency of adverse events related to the allergenicity of vaccines. Allergenicity is conferred by the presence of intact B-cell epitopes that crosslink allergen-specific IgE on effector cells. The use of linear peptide sequences representing fragments of the native allergen is one approach to reduce allergenicity. Preclinical models of peptide immunotherapy have demonstrated efficacy in both autoimmunity and allergy. Translation of this technology into the clinic has gained momentum in recent years based on encouraging results from early clinical trials. To date, efforts have focused on two major allergens, but vaccines to a broader range of molecules are currently in clinical development. Mechanistically, peptide immunotherapy appears to work through the induction of adaptive, allergen-specific regulatory T cells that secrete the immunoregulatory cytokine IL-10. There is also evidence that peptide immunotherapy targeting allergen-specific T cells can indirectly modulate allergen-specific B-cell responses. Peptide immunotherapy may provide a safe and efficacious alternative to conventional subcutaneous and/or sublingual approaches using native allergen preparations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".