Inhaler Devices for Delivery of LABA/LAMA Fixed-Dose Combinations in Patients with COPD
Bibliographic record
Abstract
Inhaled fixed-dose combinations (FDCs) of a long-acting β-agonist (LABA) and a long-acting muscarinic antagonist (LAMA) have become the cornerstone for the maintenance treatment of symptomatic COPD patients. In this regard, global COPD treatment guidelines have recognized the importance of inhaler devices as integral contributors to the effectiveness of LABA/LAMA FDCs and recommend regular assessment of inhaler device use by the patients in order to improve long-term clinical outcomes. Optimal disease control is also highly dependent upon patient preferences and adherence to inhaler devices. This review objectively examines and compares the major inhaler devices used to deliver different LABA/LAMA FDCs, discusses the inhaler device characteristics that determine drug deposition in the airways, real-life preference for inhaler devices, and handling of inhaler devices that impact the results of the long-term management of COPD. The introduction of new LABA/LAMA FDCs, new inhaler devices, and more clinical studies have created confusion among physicians in choosing the optimal inhaled therapy for COPD patients; in this context, this review attempts to provide an evidence-based framework for informed decision-making with a particular focus on the inhaler devices.Funding. The preparation of this manuscript was funded by Novartis Pharma AG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".