Effect of indacaterol/glycopyrronium versus salmeterol/fluticasone on hypothalamic pituitary-adrenal axis function in moderate-to-very severe COPD patients: Results from the FLAME study
Bibliographic record
Abstract
Introduction As per GOLD strategy, ICS combined with a LABA and/or a LAMA is a first line choice to treat COPD patients (pts) at a high risk of exacerbations. However, there is widespread use of ICS in COPD pts who do not meet the GOLD criteria for its use. ICS use has been associated with systemic side effects such as suppression of hypothalamic pituitary-adrenal (HPA) axis function (decreased production of endogenous glucocorticosteriods). This reflects the systemic burden of ICS which can be associated with long-term consequences. Here, we present results for indacaterol/glycopyrronium (IND/GLY; LABA/LAMA) vs. salmeterol/fluticasone (SFC; LABA/ICS) in terms of HPA-axis function from the FLAME study. Methods FLAME was a 52-week, multicentre, double-blind study that randomised (1:1) moderate-to-very severe COPD pts (postbronchodilator FEV1 ≥25 and <60% predicted and ≥1 exacerbations in the previous year) to receive either IND/GLY 110/50µg o.d. or SFC 50/500µg b.i.d. HPA –axis function was assessed by measuring 24-h urine cortisol level in a subset of pts. Results Of 3362 randomised pts, 535 were included in the urine cortisol set. Median urine cortisol level decreased by 8.67% with SFC treatment, while the level increased by 1.42% with IND/GLY treatment from baseline at Week 52. Consistently, median cortisol/creatinine ratio reduced with SFC (by 10.39%) and increased with IND/GLY (by 5.62%) from baseline following 52-week treatment. Conclusion SFC but not IND/GLY treatment was associated with suppression of HPA-axis function in moderate-to-very severe COPD patients, confirming systemic effects of SFC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".