Matching Inhaler Devices with Patients: The Role of the Primary Care Physician
Bibliographic record
Abstract
Poor inhaler technique and nonadherence impair the efficacy of medications for asthma and chronic obstructive pulmonary disease (COPD). A range of factors, including age, dexterity, inspiratory capacity, cognitive ability, health literacy, and ethnicity, can impact a patient's ability and intention to use their device. Treatment success can also be influenced by patient preferences and perceptions. Therefore, it is important that healthcare professionals effectively match inhaler devices to individual patients' needs and abilities and empower patients by including them in treatment decisions. Physicians must, therefore, fully understand the characteristics of each device, as well as their patients' demographic characteristics and comorbidities. Following device selection, patient training and education, including a physical demonstration of the device, are key to eliminate any critical errors that may impact on health outcomes. Inhaler technique should be frequently rechecked. This review will examine the important role of primary care providers in the selection of appropriate inhaler devices and provision of training for patients with COPD and asthma to optimize correct inhaler use and adherence. An overview of the key features of available devices and of the factors to consider when selecting devices will be provided in the context of current asthma and COPD guidelines.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".