The current evidence regarding the efficacy of tezepelumab administered for asthma on T2-related comorbidities
Bibliographic record
Abstract
INTRODUCTION: T2-comorbidities are the most common in severe asthma (SA) patients and have a negative impact on disease outcomes but also an important socio-economic burden. Treating SA and its comorbidities by one medication is a very exciting possibility for the clinicians. Several biologics used for SA showed benefits on T2-comorbidities, but currently more limited data exists for tezepelumab, most recently developed in this domain. AREAS COVERED: This paper summarizes the available evidence regarding the efficacy of tezepelumab on T2-comorbidities of SA. Electronic search queries were applied to PubMed and Medline databases by using the following terms: 'tezepelumab,' 'severe asthma,' 'allergic rhinitis' (AR), 'chronic rhinosinusitis,' 'nasal polyps' (CRSwNP), 'aspirin exacerbated disease (AERD),' 'atopic dermatitis' (AD), 'eczema,' 'chronique spontaneous urticaria' (CSU),'food allergy' (FA), 'eosinophilic esophagitis' (EE). EXPERT OPINION: Tezepelumab treatment showed undeniable benefits on CRSwNP and AERD by improving sino-nasal and asthma outcomes. If the efficacy of tezepelumab on severe allergic asthma is well documented, current data are insufficient to conclude on its impact on AR. The effects of tezepelumab on AD and CSU were disappointing. No consistent data exists regarding FA and EE. Future studies are needed to confirm the efficacy of tezepelumab on AR, FA, and EE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".