MétaCan
Menu
Back to cohort

Pharmacologic and anti‐IgE treatment of allergic rhinitis ARIA update (in collaboration with GA<sup>2</sup>LEN)

2006· review· en· W2130846008 on OpenAlexaff
Jean Bousquet, Paul Van Cauwenberge, Claus Bachert, Carlos E. Baena‐Cagnani, J. Bouchard, Chaweewan Bunnag, Giorgio Walter Canonica, K.‐H. Carlsen, Yizhang Chen, Álvaro A. Cruz, Adnan Čustović, Pascal Demoly, R. Dubakiene, S. R. Durham, W. J. Fokkens, Peter Howarth, James P. Kemp, Marek L. Kowalski, Violeta Kvedarienė, Brian J. Lipworth, Richard F. Lockey, V J Lund, S. Mavale‐Manuel, Eli O. Meltzer, Joaquim Mullol, Robert M. Naclerio, K Nékám, Ken Ohta, Nikolaos G. Papadopoulos, G. Passalacqua, R. Pawankar, Todor A. Popov, P. Potter, David Price, G. K. Scadding, F. Estelle R. Simons, V Špičák, Erkka Valovirta, De Yun Wang, Barbara P. Yawn, Osman Yusuf

Bibliographic record

VenueAllergy · 2006
Typereview
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaSt. Joseph's Hospital
Fundersnot available
KeywordsMedicineImmunoglobulin EDermatologyMEDLINEAllergyImmunologyAntibodyBiology

Abstract

fetched live from OpenAlex

The pharmacologic treatment of allergic rhinitis proposed by ARIA is an evidence-based and step-wise approach based on the classification of the symptoms. The ARIA workshop, held in December 1999, published a report in 2001 and new information has subsequently been published. The initial ARIA document lacked some important information on several issues. This document updates the ARIA sections on the pharmacologic and anti-IgE treatments of allergic rhinitis. Literature published between January 2000 and December 2004 has been included. Only a few studies assessing nasal and non-nasal symptoms are presented as these will be discussed in a separate document.

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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.028
GPT teacher head0.306
Teacher spread0.278 · 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

Citations125
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAllergySame topicAllergic Rhinitis and SensitizationFrench-language works237,207