Improving the Efficiency of Respiratory Drug Delivery: A Review of Current Treatment Trends and Future Strategies for Asthma and Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous airway diseases associated with significant morbidity and mortality. Pharmacological treatment is delivered primarily through the inhalation route using various devices. Optimal disease control is highly dependent upon patient adherence. Both patients with asthma and COPD are prone to exacerbations leading to hospitalization, which can significantly impact quality of life. Poor adherence is a complex and multifactorial problem that does not have one simple solution. However, it is the biggest risk factor for exacerbations and consequently high healthcare utilization. This review discusses the complex and multifactorial obstacles that impact patient adherence as well as the effect on overall treatment outcomes and healthcare utilization. We also critically examined and compared relatively recent improvements in breath-activated pressurized metered dose inhalers, dry powder inhalers, and e-technology in asthma and COPD. Finally, future treatment strategies for better patient compliance such as personalized medicine and the importance of decision-making between patients and physicians were highlighted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".