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Record W2974946068 · doi:10.1183/13993003.00588-2019

Management of severe asthma: a European Respiratory Society/American Thoracic Society guideline

2019· article· en· W2974946068 on OpenAlexaff
Fernando Holguín, Juan Carlos Cardet, Kian Fan Chung, Sarah Diver, Diogenes S. Ferreira, Anne M. Fitzpatrick, Mina Gaga, Liz Kellermeyer, Sandhya Khurana, Shandra L. Knight, Vanessa M. McDonald, Rebecca L. Morgan, Victor E. Ortega, David Rigau, Padmaja Subbarao, Thomy Tonia, Ian M. Adcock, Eugene R. Bleecker, Christopher E. Brightling, Louis‐Philippe Boulet, Michael D. Cabana, Mario Castro, Pascal Chanez, Adnan Čustović, Ratko Djukanović, Urs Frey, Betty Frankemölle, Peter G. Gibson, Dominique Hamerlijnck, Nizar N. Jarjour, Satoshi Konno, Huahao Shen, Cathy Vitary

Bibliographic record

VenueEuropean Respiratory Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversité LavalSickKids FoundationMcMaster UniversityImpact
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthJapan Allergy FoundationAmerican Thoracic SocietyKyorin PharmaceuticalEuropean Respiratory SocietySanofiNational Institute for Health and Care ResearchTeva Pharmaceutical IndustriesMinistry of Education, Culture, Sports, Science and TechnologyAstraZenecaGlaxoSmithKline
KeywordsMedicineExhaled nitric oxideAsthmaGuidelineTask forceAsthma managementIntensive care medicineChronic coughGrading (engineering)Psychological interventionEosinophilPhysical therapyImmunologyBronchoconstrictionPathology

Abstract

fetched live from OpenAlex

This document provides clinical recommendations for the management of severe asthma. Comprehensive evidence syntheses, including meta-analyses, were performed to summarise all available evidence relevant to the European Respiratory Society/American Thoracic Society Task Force's questions. The evidence was appraised using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach and the results were summarised in evidence profiles. The evidence syntheses were discussed and recommendations formulated by a multidisciplinary Task Force of asthma experts, who made specific recommendations on six specific questions. After considering the balance of desirable and undesirable consequences, quality of evidence, feasibility, and acceptability of various interventions, the Task Force made the following recommendations: 1) suggest using anti-interleukin (IL)-5 and anti-IL-5 receptor α for severe uncontrolled adult eosinophilic asthma phenotypes; 2) suggest using a blood eosinophil cut-point ≥150 μL −1 to guide anti-IL-5 initiation in adult patients with severe asthma; 3) suggest considering specific eosinophil (≥260 μL −1 ) and exhaled nitric oxide fraction (≥19.5 ppb) cut-offs to identify adolescents or adults with the greatest likelihood of response to anti-IgE therapy; 4) suggest using inhaled tiotropium for adolescents and adults with severe uncontrolled asthma despite Global Initiative for Asthma (GINA) step 4–5 or National Asthma Education and Prevention Program (NAEPP) step 5 therapies; 5) suggest a trial of chronic macrolide therapy to reduce asthma exacerbations in persistently symptomatic or uncontrolled patients on GINA step 5 or NAEPP step 5 therapies, irrespective of asthma phenotype; and 6) suggest using anti-IL-4/13 for adult patients with severe eosinophilic asthma and for those with severe corticosteroid-dependent asthma regardless of blood eosinophil levels. These recommendations should be reconsidered as new evidence becomes available.

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)0.010
metaresearch head score (Gemma)0.010
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: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0060.006

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 head0.024
GPT teacher head0.310
Teacher spread0.285 · 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
GenreMethods

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

Citations614
Published2019
Admission routes1
Has abstractyes

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