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Allergic rhinitis management pocket reference 2008*

2008· article· en· W2098616185 on OpenAlexaff
Jean Bousquet, Jim Reid, Chris van Weel, C. Baena Cagnani, Giorgio Walter Canonica, Pascal Demoly, Judah A. Denburg, W. J. Fokkens, L. Grouse, K. Mullol, Ken Ohta, Tjard Schermer, Erkka Valovirta, N. Zhong, Torsten Zuberbier

Bibliographic record

VenueAllergy · 2008
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsMcMaster University
FundersSanofiGlaxoSmithKline
KeywordsMedicineRespiratory HypersensitivityAllergyAsthmaBronchodilator AgentsImmunologyDermatologyBronchodilator

Abstract

fetched live from OpenAlex

Allergic rhinitis is a major chronic respiratory disease because of its prevalence, impacts on quality of life and work/school performance, economic burden, and links with asthma. Family doctors (also known as 'primary care physicians' or 'general practitioners') play a major role in the management of allergic rhinitis as they make the diagnosis, start the treatment, give the relevant information, and monitor most of the patients. Disease management that follows evidence-based practice guidelines yields better patient results, but such guidelines are often complicated and may recommend the use of resources not available in the family practice setting. A joint expert panel of the World Organization of Family Doctors (Wonca), the International Primary Care Airways Group (IPAG) and the International Primary Care Respiratory Group (IPCRG), offers support to family doctors worldwide by distilling the globally accepted, evidence-based recommendations from the Allergic Rhinitis and its Impact on Asthma (ARIA) initiative into this brief reference guide. This guide provides tools intended to supplement a thorough history taking and the clinician's professional judgment in order to provide the best possible care for patients with allergic rhinitis. A diagnostic Questionnaire specifically focuses the physician's attention on key symptoms and markers of the disease. When questionnaire responses suggest a diagnosis of allergic rhinitis, a Diagnosis Guide and a simple flowchart then lead the clinician through a series of investigations commonly available in primary care to support the diagnosis. In addition, key aspects of differential diagnosis are illuminated. According to ARIA, allergic rhinitis may be classified as Intermittent or Persistent, and as Mild or Moderate/Severe. The classification of rhinitis determines the treatment necessary, as set out in an ARIA flowchart included in this guide. The guide also includes information about the strength of evidence for efficacy of certain rhinitis treatments, a brief discussion of pediatric aspects, and a glossary of allergic rhinitis medications to assist the clinician in making medication choices for each individual patient. Finally, many patients with allergic rhinitis also have concomitant asthma, and this must be checked. The World Organization of Family Doctors has been delegated by WHO as the group that will be taking primary responsibility for education about chronic respiratory diseases among primary care physicians globally. This document will be a major resource in this educational program.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0810.067

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 head0.034
GPT teacher head0.253
Teacher spread0.218 · 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
GenreOther

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

Citations169
Published2008
Admission routes1
Has abstractyes

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