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Record W4408074654 · doi:10.1056/nejmoa2414482

Tezepelumab in Adults with Severe Chronic Rhinosinusitis with Nasal Polyps

2025· article· en· W4408074654 on OpenAlexaff
Brian J. Lipworth, Joseph K. Han, Martin Desrosiers, Claire Hopkins, Stella E. Lee, Joaquim Mullol, Oliver Pfaar, Ting Li, Claudia Chen, Gun Almqvist, Mary Kay Margolis, Julie McLaren, Shankar Jagadeesh, J. Mackay, Ayman Megally, Åsa Hellqvist, Vaishali S. Mankad, Lila Bahadori, Sandhia S Ponnarambil

Bibliographic record

VenueNew England Journal of Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsSt. Thomas HospitalCentre Hospitalier de l’Université de Montréal
FundersAstraZenecaAmgen
KeywordsChronic rhinosinusitisNasal polypsMedicineDermatologyInternal medicineGastroenterology

Abstract

fetched live from OpenAlex

BACKGROUND: Treatment with tezepelumab has been effective for sinonasal symptoms in patients with severe, uncontrolled asthma and a history of chronic rhinosinusitis with nasal polyps, but its efficacy and safety in adults with severe, uncontrolled chronic rhinosinusitis with nasal polyps is unknown. METHODS: We randomly assigned adults with physician-diagnosed, symptomatic, severe chronic rhinosinusitis with nasal polyps to receive standard care and either tezepelumab (at a dose of 210 mg) or placebo subcutaneously every 4 weeks for 52 weeks. The coprimary end points were the changes from baseline in the total nasal-polyp score (range, 0 to 4 [for each nostril]; higher scores indicate greater severity) and the mean nasal-congestion score (range, 0 to 3; higher scores indicate greater severity) at week 52. Key secondary end points assessed in the overall population were the loss-of-smell score, the total score on the Sinonasal Outcome Test (SNOT-22; range, 0 to 110; higher scores indicate greater severity), the Lund-Mackay score (range, 0 to 24; higher scores indicate greater severity), the total symptom score (range, 0 to 24; higher scores indicate greater severity), and the first decision to treat with nasal-polyp surgery or use of systemic glucocorticoid therapy, or both, assessed in time-to-event analyses (individual and composite). RESULTS: In total, 203 patients were assigned to receive tezepelumab and 205 to receive placebo. At week 52, the patients who received tezepelumab had significant improvements in the total nasal-polyp score (mean difference vs. placebo, -2.07; 95% confidence interval [CI], -2.39 to -1.74) and the mean nasal-congestion score (-1.03; 95% CI, -1.20 to -0.86) (P<0.001 for both scores). Tezepelumab significantly improved the loss-of-smell score (mean difference vs. placebo, -1.00; 95% CI, -1.18 to -0.83), SNOT-22 total score (-27.26; 95% CI, -32.32 to -22.21), Lund-Mackay score (-5.72; 95% CI, -6.39 to -5.06), and total symptom score (-6.89; 95% CI, -8.02 to -5.76) (P<0.001 for all scores). Surgery for nasal polyps was indicated in significantly fewer patients in the tezepelumab group (0.5%) than in the placebo group (22.1%) (hazard ratio, 0.02; 95% CI, 0.00 to 0.09); there was significantly less use of systemic glucocorticoids with tezepelumab (5.2%) than with placebo (18.3%) (hazard ratio, 0.12; 95% CI, 0.04 to 0.27) (P<0.001 for both time-to-event analyses). CONCLUSIONS: Tezepelumab therapy led to significantly greater reductions in the size of nasal polyps, the severity of nasal congestion and sinonasal symptoms, and the use of nasal-polyp surgery and systemic glucocorticoids than placebo in adults with severe, uncontrolled chronic rhinosinusitis with nasal polyps. (Funded by AstraZeneca and Amgen; WAYPOINT ClinicalTrials.gov number, NCT04851964.).

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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 designRandomized 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

Citations145
Published2025
Admission routes1
Has abstractyes

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