Bibliographic record
Abstract
The quality of the aerosol generated by a nebulizer system is a function of its design, operating parameters, and the drug formulation to be aerosolized. The aerosolized drug dose inhaled from a nebulizer is determined by the patient's breathing pattern. The site of deposition of the aerosol in the lung is primarily influenced by the inspiratory flow rate and additionally by the nature of the lung disease. Thus, tests of performance that incorporate these different variables will provide data that give a better understanding of overall nebulizer performance. As discussed in this paper, a number of in vitro and in vivo laboratory tests can be undertaken to measure the characteristics of the delivery system as well as the quality and quantity of the aerosolized drug provided. With this information one can estimate the dose of drug that will be inhaled and deposited below the larynx. The accuracy of these predictions can additionally be improved with the use of breath simulators and standard breath patterns. Breath monitors that capture and feed actual patient breathing patterns into the simulator to mimic nebulizer operation during actual patient use further increase the accuracy of dose estimation. With the vast number of nebulizers available and also in development, a comparison of information obtained from different nebulizers is key in making an informed decision when selecting an aerosol delivery system that can provide an efficacious dose of a particular drug to a specific patient population.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".