The Challenges of Quantitative Measurement of Lung Deposition Using <sup>99m</sup> Tc-DTPA from Delivery Systems with Very Different Delivery Times
Bibliographic record
Abstract
In quantifying aerosol delivery, the drug is often mixed with a radiolabel such as (99m)Tc-DTPA whose deposition is used as a proxy for the drug. (99m)Tc-DTPA deposited in the lung is cleared by a combination of absorption into the pulmonary circulation and mucociliary clearance. If administration is not instantaneous, the image will not include that clearance during administration, a problem raised if comparing devices with different administration times. However, if rates of clearance are measured, it will be possible to "correct" the initial image for the clearance that occurred during administration and before counting. Five adult males inhaled a 5-mL solution containing (99m)Tc-DTPA from a breath enhanced jet nebulizer (LC Plus)over the course of 10 min and a 1.25-mL solution from a vibrating membrane device (eFlow), which was delivered in 2.5 min. Quality assurance was the radioactivity count balance (RCB) defined as the difference in the total radioactivity pre-nebulization less post, divided by pre, and expressed as a percentage. Attenuation calculations used a (57)Co flood source (Macey and Marshall). The "correction" for the clearance of (99m)Tc-DTPA was 0.91 +/- 0.04 (mean +/- SD) for the LC Plus) and 0.96 +/- 0.02 for the eFlow). RCB was -0.6 +/- 3.5% for the LC Plus and -4.7 +/- 6.4% for the eFlow, implying acceptable accuracy. For the LC Plus, lung deposition was 15.9(13.4, 18.4)% (mean and 95% CI) of the charge dose, and for the eFlow it was 32.0(29.0, 35.0)%. This technique gave an acceptable level of accuracy for quantitative planar imaging and allowed the comparison of delivery from devices with very different rates of delivery.
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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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".