LABA/LAMA combination, exercise and lung hyperinflation in COPD: a meta-analysis
Bibliographic record
Abstract
Background: The ability to exercise is an important clinical outcome in COPD, and the improvement in exercise capacity is recognized to be an important goal in the management of COPD. Aim: We carried out a meta-analysis to evaluate the impact of LABA/LAMA combination on exercise capacity and lung hyperinflation in COPD. Methods: Randomized Clinical trials (RCTs) were identified after a search in different databases of published and unpublished trials in order to assess the impact of LABA/LAMA combinations on endurance time (ET) and inspiratory capacity (IC), vs. monocomponents in COPD patients. Results: Eight RCTs including 1,632 COPD patients were meta-analyzed. LABA/LAMA combinations were significantly (P<0.05) more effective than the LABA or LAMA alone in terms of the improvement in ET (+43 sec and +22 sec, respectively) and IC (+107 ml and +87ml, respectively). LABA/LAMA combinations showed the highest probability of being the best therapy with regard of both ET and IC (100% and 100%, respectively), followed by LAMA (66% and 64%, respectively) and LABA (32% and 36%, respectively), as indicated by the analysis of surface under the cumulative ranking curve (SUCRA). No publication bias was detected in this meta-analysis. Conclusions: This synthesis clearly demonstrates that if the goal of the therapy was to enhance exercise capacity in patients with COPD, LABA/LAMA combination consistently met the putative clinically meaningful differences for both ET and IC and, in this respect, was superior to the monocomponents.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".