18F-Fluorodeoxyglucose Positron Emission Tomographic Imaging of Pulmonary Functions, Pathology, and Drug Delivery
Bibliographic record
Abstract
18F-FDG positron emission tomographic (PET) scanning is a major imaging tool widely used to investigate lung function and lung disease. Tomographic imaging of drug delivered to the lung via the aerosol route can provide data that link the regional distribution and pharmacokinetics of a specific drug to clinical efficacy. Correlation with routine clinical functional measurements is possible, but, whereas 3D imaging data provides local drug deposition information, clinical tests of respiratory status are "black-box" measurements with outcomes specific to large or small airways inferred from the results. However, biopsies may be obtained directly from the tissue being imaged and therefore allow correlations with tracer uptake in the particular tissues. Imaging a radiolabeled pharmaceutic over time provides temporal information of receptor binding, drug absorption, or drug clearance from airways or the alveolar space. Changes in the deposition of inhaled aerosols within the lung related to the presence of disease or resulting from inhalation challenge interventions or inhaled therapies can be visualized with PET and may correlate with clinical outcomes. As well, the amount of an inhaled tracer deposited in various regions of the lung can give an indication of the efficiency of drug delivery and, combined with the regional distribution of the drug within the lung and the rate of drug absorption, estimate clinical efficacy and safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".