Laser Diffractometry as a Technique for the Rapid Assessment of Aerosol Particle Size from Inhalers
Bibliographic record
Abstract
The rapid assessment of aerosols produced by medicinal inhalers is highly desirable from several standpoints, including the assurance of product quality, the development of new delivery systems, and the need to meet an increasing requirement by regulatory bodies for reliable in vitro performance data. Particle size analysis has traditionally been undertaken by cascade impactor on account of the direct assessment of active pharmaceutical ingredient(s) (APIs) that is possible by this method. However, laser diffractometry is less labor-intensive, more rapid, and can be a less invasive procedure. The technique provides meaningful results; as long as precautions are taken to validate that the measurements are an accurate reflection of the distribution of API mass as a function of particle or droplet size. We begin the review by examining the underlying theory of the laser diffraction method. After a brief description of current laser diffractometers used in inhaler measurements, we continue by examining the range of applications by inhaler class. We then examine the basis upon which inhaler measurements made by laser-diffractometry can be compared with equivalent particle size distribution data from compendial techniques. We conclude the assessment of the technique by developing guidelines for its valid application as a component of the range of in vitro methods that are available for inhaler performance assessment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".