Oral corticosteroid (OCS)-sparing effect of tralokinumab in severe, uncontrolled asthma: the TROPOS study
Bibliographic record
Abstract
Background: Long-term OCS use can cause adverse effects in severe asthma patients (pts). Aim: To evaluate OCS-sparing effects of tralokinumab (tralo) vs placebo (pbo) in pts with severe, uncontrolled asthma on maintenance ICS/LABA and OCS therapy. Methods: Pts entered either a 2-week (wk) run-in period (pts with OCS-dose reduction failure <6 months) or a 2-wk run-in period plus an up to 8-wk dose optimisation period (pts without OCS-dose reduction). Eligible pts were randomised 1:1 to tralo 300 mg or pbo Q2W SC. Primary endpoint was percent change from baseline in the average OCS dose at Wk 40 while preserving asthma control. Secondary endpoints were between-group differences in proportion of pts with a final daily average OCS dose ≤5 mg at Wk 40, proportion of pts with ≥50% reduction in OCS maintenance dose and asthma exacerbation rate. Lung function was an exploratory endpoint. Results: Of 140 randomised and treated pts (mean age, 55 yrs; male, 38%), 92% completed the study. At Wk 40, no significant between-group differences were reported for the primary or secondary endpoints (Table). Findings were similar in a subset of pts with IL-13-driven disease (FeNO ≥37 ppb). In the exploratory endpoint of pre-bronchodilator FEV1, an increase of 170 mL (vs pbo) was observed. No unexpected safety findings were reported. Conclusion: Tralo did not provide significant OCS-sparing benefits vs pbo in severe asthma pts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".