Choosing the right inhaler for the right patient: Considerations for effective management of patients with chronic obstructive pulmonary disease or asthma
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Effective management of chronic respiratory disorders such as chronic obstructive pulmonary disease and asthma necessitates that patients inhale their medication. However, lack of detailed guidelines on the technological and mechanical functions of inhalers limits the ability of health care providers (HCPs) to personalize inhaler choice for patients. Numerous types of inhalers are currently available which offer their own distinct advantages and disadvantages. Independent of the drug class, the choice of inhaler may be influenced by many factors (e.g., inhaler attributes and the efficiency with which it delivers the medication, patient characteristics and preferences, dosing regimen, clinical setting, and support available for both patients and HCPs). This article attempts to summarize the inhalation technology and factors influencing inhaler choice and use and to provide an approach for matching the right inhaler to the right patient. CONCLUSIONS: Identifying factors related to inhaler choice is critical to ensuring adherence to treatment and patients' ability to use their inhaler correctly. IMPLICATIONS FOR PRACTICE: This review will help HCPs engage their patients in decision-making for inhaler choice and facilitate selection of the correct inhaler for each patient (i.e., one that they will use).
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".