Patient Focus and Regulatory Considerations for Inhalation Device Design: Report from the 2015 IPAC-RS/ISAM Workshop
Bibliographic record
Abstract
This article reports on discussions at the 2015 workshop cosponsored by the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) and the International Society for Aerosols in Medicine (ISAM), entitled "Regulatory and Patient Considerations for Inhalation Device Design, Development and Use." Key topics addressed at the workshop and presented here include patient-focused device design for orally inhaled products (OIPs), instructions for use (IFU), human factors, regulatory considerations in the United States and Europe, development of generic inhalers, quality-by-design, and change management of OIP devices. Workshop participants also identified several areas for further consideration and emphasized the need for increased focus on the patient to create therapeutic products (inclusive of device design, IFU, education, training) that support adherence with an individual patient's treatment regimen. Advances in patient-centric product development will require engagement and collaboration by industry, regulators, patients, physicians, and other stakeholders. The article includes summaries of presented talks as well as of panel and audience discussions.
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 | 0.109 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".