“Trying, But Failing” — The Role of Inhaler Technique and Mode of Delivery in Respiratory Medication Adherence
Bibliographic record
Abstract
Inhaled therapies are the backbone of asthma and chronic obstructive pulmonary disease management, helping to target therapy at the airways. Adherence to prescribed treatment is necessary to ensure achievement of the clinician's desired therapeutic effect. In the case of inhaled therapies, this requires patients' acceptance of their need for inhaled therapy together with successful mastery of the inhaler technique specific to their device(s). This article reviews a number of challenges and barriers that inhaled mode of delivery can pose to optimum adherence-to therapy initiation and, thereafter, to successful implementation and persistence. The potential effects on adherence of different categories of devices, their use in multiplicity, and the mixing of device categories are discussed. Common inhaler errors identified by the international Implementing Helping Asthma in Real People (iHARP) study are summarized, and adherence intervention opportunities for health care professionals are offered. Better knowledge of common errors can help practicing clinicians identify their occurrence among patients and prompt remedial actions, such as tailored education, inhaler technique retraining, and/or shared decision making with patients regarding suitable alternatives. Optimizing existing therapy delivery, or switching to a suitable alternative, can help avoid unnecessary escalation of treatment and health care resources.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".