Bibliographic record
Abstract
In vivo measurements of the deposition of an inhaled radiolabeled pharmaceutic have provided useful information related to the inhaler efficiency for depositing drug in the lung. A number of labeling techniques have been developed and applied to pharmaceutical aerosols delivered by pressurized metered-dose inhalers (pMDIs), dry powder inhalers (DPIs) and nebulizers; the choice of radiotracer depends on the type of imaging study being performed and the equipment used to image the lung. Preparation, validation and calibration of the radiolabeled pharmaceutical product is key to successful interpretation of the imaging study. When imaging a subject after inhalation of a radiolabeled formulation, it is the radioactivity that is detected and measured by the scanner; absolute amounts of deposited drug are inferred from the counts of radioactivity in the lung and other regions, based on the assumption that there is a 1:1 relationship between the two components—drug and radioactivity. This relationship holds true for direct-labeled PET products or for those formulations where a firm bond can be demonstrated between the drug and radiotracer for the time taken to acquire all the images. This chapter will discuss radiolabeling methods applied to therapeutic aerosols for the purpose of determining the deposition efficiency of these aerosols in the lung. The techniques apply to both in vivo studies in man and in animal models, and to some extent to in vitro models.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".