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Record W2951877381 · doi:10.1186/s13223-019-0348-z

Outcomes following mepolizumab treatment discontinuation: real-world experience from an open-label trial

2019· article· en· W2951877381 on OpenAlexaffvenue
Héctor Ortega, Catherine Lemière, Jean‐Pierre Llanos, Mark Forshag, Robert G. Price, Frank C. Albers, Steven W. Yancey, Mario Castro

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2019
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersGlaxoSmithKline
KeywordsMepolizumabMedicineDiscontinuationPost-hoc analysisAsthmaOpen labelClinical trialEosinophilPost hocInternal medicineRandomized controlled trialPhysical therapyPediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

Limited information is available on the clinical course of patients with severe asthma following discontinuation of biologic treatment. Therefore, a post hoc analysis was conducted in patients with severe eosinophilic asthma who participated in the COSMOS trial, where patients received mepolizumab for more than 1 year of continuous therapy. The objective of this post hoc analysis was to evaluate changes in the Asthma Control Questionnaire (ACQ-5) and blood eosinophil counts 12 weeks after the last administration of mepolizumab. Cessation of mepolizumab was associated with a rise in the blood eosinophil count and loss of asthma control after stopping therapy. These data suggest that patients with severe disease require extended and continuous treatment. Further studies evaluating longer duration of continuous treatment with mepolizumab could help understanding of whether changes in the presentation of the disease (disease modification) are possible with the use of biologics, such as mepolizumab.

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)0.019
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.422
Teacher spread0.357 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations41
Published2019
Admission routes2
Has abstractyes

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