Oral inhalation therapy: meeting the challenge of developing more patient-appropriate devices
Bibliographic record
Abstract
Although oral inhalers have been mass produced for more than 50 years, there is a large body of literature in which evidence has been provided that patients either misuse their inhalers inadvertently or deliberately, thereby reducing their intended efficacy or, in the worst cases, rendering them altogether ineffective. In general, inhalers are becoming increasingly complicated with the incorporation of add-on devices, miniaturized electronics and ever more complex mechanical systems that aid aerosol delivery to the lower respiratory tract and, at the same time provide user feedback. However, these benefits often come at a significant cost, and there are signs that increasing attention will need to be given to the cost-benefit equation in the future. This review explores the development of pressurized metered-dose inhalers, dry powder inhalers and devices for liquid-droplet dispersal and inhalation from the perspective of the patient, by focusing on aspects that improve user interaction. These include designed-in features, such as breath-enhanced or breath-actuated operation that interact with the breathing pattern of the user, as well as more direct feedback aids that confirm, to the patient or healthcare provider that the dose has been delivered and that the patient has inhaled.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".