Understanding pressurized metered dose inhaler performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Deepening the current understanding of the factors governing the performance of the pressurized metered dose inhaler (pMDI) has the potential to benefit patients by providing improved drugs for current indications as well as by enabling new areas of therapy. Although a great deal of work has been conducted to this end, our knowledge of the physical mechanisms that drive pMDI performance remains incomplete. AREAS COVERED: This review focuses on research into the influence of device and formulation variables on pMDI performance metrics. Literature in the areas of dose metering, atomization and aerosol evolution and deposition is covered, with an emphasis on studies of a more fundamental nature. Simple models which may be of use to those developing pMDI products are summarized. EXPERT OPINION: Although researchers have had good success utilizing an empirically developed knowledge base to predict pMDI performance, such knowledge may not be applicable when pursuing innovations in device or formulation technology. Developing a better understanding of the underlying mechanisms is a worthwhile investment for those working to enable the next generation of pMDI products.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it