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Record W2581300200 · doi:10.1111/all.13135

Novel approaches and perspectives in allergen immunotherapy

2017· review· en· W2581300200 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAllergy · 2017
Typereview
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsMcMaster University
FundersAllergopharmaRegeneron PharmaceuticalsNovartisMerck
KeywordsMedicineAllergen immunotherapyPublic healthClinical trialImmunotherapyHarmonizationAllergyImmunologyIntensive care medicineAllergenPathology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.251
GPT teacher head0.355
Teacher spread0.104 · 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