Measuring Total and Regional Lung Deposition Using Inhaled Radiotracers
Bibliographic record
Abstract
The delivery of an inhaled drug to the lungs can be measured by adding a gamma-emitting radiotracer to the formulation and using two-dimensional planar imaging or three-dimensional single photon emission computerized tomography (SPECT) to provide detailed information on lung deposition. The isotope most commonly used is the low energy (140 KeV) isotope, 99m technetium. Radiolabeling techniques have been successfully developed for use with nebulizers, pressurized metered dose inhalers (pMDIs), and dry powder inhaler formulations (DPI), and to investigate drug delivery to the respiratory tract for a variety of drug formulations and patient populations. However, for pMDIs and DPIs, the radiotracer is usually only physically associated with, rather than chemically bound, to the drug. Therefore, once deposited, the radiotracer may disassociate from the drug and cannot be used to track its subsequent fate; however, incorporation of a radiotracer directly into the drug molecule can overcome this. By using positron emitters such as 11carbon or 18fluorine it is possible to generate three-dimensional images of the drug in the lung using positron emission tomography (PET) scanning, which has a higher resolution and is more accurate than SPECT. Labeling drugs with PET emitters is more complex as the drug molecule must first be synthesized to contain the radioactive isotope before the drug is formulated for the inhaler. As with gamma-scintigraphy, PET scanning can be used to investigate physiological changes in the lung following therapeutic intervention, but as biological radiotracers are used, functional images (i.e., of the drug's uptake and metabolism) can also be obtained.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".