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Record W1877156325 · doi:10.1186/s40413-015-0078-8

Allergen immunotherapy on the way to product-based evaluation—a WAO statement

2015· article· en· W1877156325 on OpenAlexaff
Claus Bachert, Mark Larché, С. Бонини, Giorgio Walter Canonica, Thomas M. Kündig, Désirée Larenas‐Linnemann, Dennis K. Ledford, Hugo Neffen, Ruby Pawankar, Giovanni Passalacqua

Bibliographic record

VenueWorld Allergy Organization Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsMedicineAsthmaAllergen immunotherapySlitImmunotherapyAllergenAllergic asthmaDiseaseImmunologyClinical efficacyIntensive care medicineAllergyDermatologyInternal medicineImmune systemPsychology

Abstract

fetched live from OpenAlex

Allergen immunotherapy (AIT) is widely used in clinical practice for patients with moderate to severe allergic rhinitis due to inhalant allergens and may be delivered via subcutaneous (SCIT) and sublingual routes (SLIT). However, the quality of evidence for individual AIT products is very heterogeneous, and extensions of overall conclusions ("class effects") on the efficacy and disease-modifying effects to all AIT products are unjustified. In contrast, each product needs to be evaluated individually, based on available study results, to justify efficacy and specific claims on sustained and disease modifying effects per allergen and targeted patient group (children vs. adults, allergic rhinitis vs. asthma). WAO intends to support the current development to evidence-based AIT, which ultimately will lead to a more efficacious treatment of allergic patients and the appropriate recognition of AIT.

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)0.084
metaresearch head score (Gemma)0.081
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.084
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.081
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0020.002
Science and technology studies0.0040.009
Scholarly communication0.0110.008
Open science0.0060.009
Research integrity0.0680.039
Insufficient payload (model declined to judge)0.0040.006

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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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
GenreEditorial

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

Citations90
Published2015
Admission routes1
Has abstractyes

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