Mepolizumab improves lung function under real-world settings in REALITI-A study
Bibliographic record
Abstract
Introduction: Mepolizumab (MEPO) improves lung function in clinical trials but patient selection for these studies is restricted. No restrictions apply in standard clinical practice, and data from real-life settings is limited. Aims: To evaluate the effectiveness of MEPO in improving lung function in real-life settings. Methods: REALITI-A enrolled patients with severe eosinophilic asthma (SEA) who initiated MEPO from 51 centers in 7 countries. FEV1 was measured by standard spirometry during routine clinic visits. Baseline FEV1 was defined as the latest measure prior to MEPO initiation. Post-initiation FEV1 were averages of FEV1 measures for each subject during the prospective observation periods. Changes from baseline were evaluated using mixed model repeated measures. FEV1 changes were also evaluated by baseline blood eosinophil counts (BEC). Results: FEV1 data was available from 298 and 174 patients at baseline and 9-12 months. Baseline FEV1 least square (LS) mean (95% CI) was 2.0L (1.9-2.1). FEV1 mean changes (95% CI) from baseline at 0-3, 3-6, 6-9 and 9-12 months were respectively 92.5 ml (38.1–147.0), 88.9 ml (29.2-148.6), 89.9 ml (24.4–155.4) and 123.3 ml (56.5–190.0). Significant increases were evident in subjects with BEC≥300 cells/µL (n=238), with a LS mean increase (95% CI) of 128.3 ml (25.7-231.0) at 9-12 months from baseline; sample size for subgroups with lower BEC is too small to draw meaningful conclusions. Conclusions: SEA patients, treated with MEPO in real life settings, demonstrated improvement in lung function that was sustained across the year of observation. This improvement was evident in those with baseline BEC of ≥300 cells/µL. Funding: GSK [204710]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".