Biomarkers and phenotyping: a holistic approach to asthma treatment with tezepelumab
Bibliographic record
Abstract
Background: Tezepelumab reduced the annualized asthma exacerbation rate (AAER) and improved lung function, symptom control and quality of life (QoL) across biomarker levels in patients with severe, uncontrolled asthma in the PATHWAY ( NCT02054130 ) and NAVIGATOR ( NCT03347279 ) studies. Objective: This post hoc analysis assessed the relationship between absolute change in biomarker levels from baseline to week 52 and asthma outcomes in PATHWAY and NAVIGATOR patients. Methods: The relationships between absolute changes in biomarkers (blood eosinophil counts, fractional exhaled nitric oxide [FeNO] levels and serum immunoglobulin E [IgE] levels) from baseline to week 52 and AAER, pre-bronchodilator (BD) forced expiratory volume in 1 second (FEV1), Asthma Control Questionnaire-6 score and Asthma Quality of Life Questionnaire score were assessed over 52 weeks using locally estimated scatterplot smoothing regression. Results: In tezepelumab recipients, changes in biomarker levels over 52 weeks were not associated with the AAER, whereas changes in FeNO levels and IgE levels correlated with improved pre-BD FEV1, and improvements in asthma symptoms and QoL, respectively (Figure). Conclusion: Changes in biomarker levels over 52 weeks were not associated with on-treatment AAER with tezepelumab treatment; however, changes in some biomarkers correlated with improvements in lung function, asthma symptoms and QoL. erj;64/suppl_68/PA3559/F1 F1 F1
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".