Randomized, Double-Blind, Dose-Finding Study for Tiotropium when Added to Olodaterol, Administered via the Respimat® Inhaler in Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
INTRODUCTION: Combining long-acting muscarinic antagonists (LAMAs) and long-acting β2-agonists (LABAs) is beneficial in chronic obstructive pulmonary disease (COPD), as the two classes of bronchodilator have complementary modes of action. The optimal dose for the fixed-dose combination of the LAMA tiotropium and the LABA olodaterol needed to be determined. In this phase II trial, the dose response of tiotropium on top of olodaterol was investigated in a free-dose combination, while other phase II studies have explored different doses of olodaterol on top of tiotropium, with both drugs delivered using the Respimat(®) inhaler. METHODS: This was a double-blind incomplete crossover trial in which 233 patients with moderate or severe COPD were randomized to receive four out of eight free-dose combinations of olodaterol (5 or 10 µg) and tiotropium (1.25, 2.5, or 5 µg) or placebo for 4 weeks each. Primary end point was trough forced expiratory volume in 1 s (FEV1) change from baseline (response) after 4 weeks. RESULTS: Addition of tiotropium 1.25, 2.5, and 5 µg to olodaterol 5 µg increased mean trough FEV1 response by 0.054, 0.065, and 0.084 L, respectively; addition of tiotropium 1.25, 2.5, and 5 µg to olodaterol 10 µg increased mean trough FEV1 response by 0.051, 0.083, and 0.080 L, respectively. All treatments were well tolerated and incidence of adverse events was similar with all treatments. CONCLUSIONS: Overall, a dose response for tiotropium on top of both doses of olodaterol was observed, with increasing improvements in trough FEV1 compared to olodaterol alone as the tiotropium dose was increased. FUNDING: Boehringer Ingelheim. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01040403.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".