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Record W2153659316 · doi:10.1186/1710-1492-3-2-53

Immunotherapy with Allergen Peptides

2007· article· en· W2153659316 on OpenAlexvenueno aff
Mark Larché

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2007
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersMedical Research CouncilAsthma and Lung UK
KeywordsImmunogenicityAllergenImmunotherapyImmunologyEpitopeMedicineImmunoglobulin EAllergen immunotherapyAllergyClinical efficacyAntibodyImmune systemInternal medicine

Abstract

fetched live from OpenAlex

: Specific allergen immunotherapy (SIT) is disease-modifying and efficacious. However, the use of whole allergen preparations is associated with frequent allergic adverse events during treatment. Many novel approaches are being designed to reduce the allergenicity of immunotherapy preparations whilst maintaining immunogenicity. One approach is the use of short synthetic peptides which representing dominant T cell epitopes of the allergen. Short peptides exhibit markedly reduced capacity to cross link IgE and activate mast cells and basophils, due to lack of tertiary structure. Murine pre-clinical studies have established the feasibility of this approach and clinical studies are currently in progress in both allergic and autoimmune diseases.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

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.

Opus teacher head0.017
GPT teacher head0.305
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2007
Admission routes1
Has abstractyes

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