Bibliographic record
Abstract
Patterns of regional aerosol deposition within the lungs are known to vary in a predictable manner with a number of factors, most notably aerodynamic particle size and inhalation pattern. Targeting deposition involves the intentional manipulation of one or more of these factors to promote aerosol deposition in certain locations within the respiratory tract. This section will begin by exploring existing evidence supporting the need to target regional deposition. Thereafter, various approaches to targeting will be introduced. In addition to control of aerodynamic particle size and inhalation pattern, a collection of approaches are available through which to passively target deposition to more central or peripheral lung regions. These include the delivery of short aerosol boluses at prescribed time points in inhalation, control of transient hygroscopic aerosol size changes during transport through the respiratory tract, and use of alternative carrier gas mixtures such as helium/oxygen mixtures. Comparatively, targeting aerosol deposition locally to very precise, spatially-defined lung regions is in its infancy. Early, exploratory techniques used for local targeting will be described. The continued evolution of deposition targeting towards ever more specific locations within the lungs is required to explore fundamental research questions in aerosol medicine: namely, how precise does targeting need to be before additional refinement fails to produce appreciably different therapeutic effects, and which nascent applications of aerosols in medicine might benefit from more selective regional targeting?
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".