Pressurized Metered-Dose Inhalers
Bibliographic record
Abstract
Pressurized Metered-dose Inhalers (pMDIs) are complex drug-device combination products that are widely used to treat lung diseases, and may also be advantageously employed to deliver active pharmaceutical ingredients (APIs) to the systemic circulation through the lungs. pMDIs occupy ca. 70% of the respiratory inhaler device market share and are expected to continue their dominance in the foreseeable future. The transition from CFCs to hydrofluoroalkane (HFA) propellants has stimulated significant advances in this field, and the proposed phase out of HFAs as indicated in the Kigali amendment to the Montreal Protocol (starting in 2019) is expected to generate renewed interest in the design of alternative propellant systems and subsequently device components and formulations compatible with the more environmentally acceptable propellant alternatives to be used in the future medicinal aerosol formulations. Development of new technologies enabling: (i) the delivery of a broader class of APIs with pMDIs including biologics; (ii) the development of orally inhaled drug products containing nanomaterials; and (iii) the delivery of higher drug doses are some of the areas that are expected to help further the use of pMDIs and thus secure a larger share for orally inhaled products in the global drug delivery market.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.085 |
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".