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Record W4412805746 · doi:10.1016/j.jacig.2025.100549

Mepolizumab for the management of chronic rhinosinusitis with nasal polyps across the United States: A retrospective study

2025· article· en· W4412805746 on OpenAlexaff
Juan Carlos Cardet, Jared Silver, Martin Maldonado-Puebla, François Laliberté, Chi Gao, Ramya Ramasubramanian, Annalise Hilts, Kaixin Zhang, Jeremiah Hwee, Waseem Ahmed, A. Edgecomb

Bibliographic record

VenueJournal of Allergy and Clinical Immunology Global · 2025
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsGlaxoSmithKline (Canada)
FundersGlaxoSmithKline
KeywordsMepolizumabRetrospective cohort studyMedicineInternal medicineEosinophil

Abstract

fetched live from OpenAlex

Background: Retrospective data are limited on the effectiveness of mepolizumab treatment that is reflective of real-world practice in patients with chronic rhinosinusitis with nasal polyps (CRSwNP). Objective: We evaluated changes in administration of nasal polyp (NP)-related oral corticosteroids (OCS) and other treatments, exacerbations, sinus surgeries, NP-related health care resource utilization, and costs before and after mepolizumab initiation in patients with CRSwNP. Methods: Retrospective cohort study using data from the Komodo Research database included adults with CRSwNP, without severe asthma, initiating mepolizumab therapy on or after July 29, 2021 (index date), with 12 months of continuous health care enrollment before index and ≥6 months after index. Treatment with reslizumab, benralizumab, or tezepelumab during the study period was excluded. Outcomes were compared pre- versus post-mepolizumab initiation for the overall population and on-label subgroup analysis. Results: < .001). Conclusion: In this first retrospective mepolizumab study for patients with CRSwNP without severe asthma, improvements in all outcomes were observed after mepolizumab initiation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.372
Teacher spread0.353 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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