A Comparison of Amount and Speed of Deposition Between the PARI LC STAR <sup>®</sup> Jet Nebulizer and an Investigational eFlow <sup>®</sup> Nebulizer
Bibliographic record
Abstract
BACKGROUND: The potency and physical properties of many of the drugs used in the treatment of cystic fibrosis necessitates the use of nebulization, a relatively time-consuming pulmonary delivery method. Newer, faster, and more efficient delivery systems are being proposed. The purposes of this study was to compare the length of time it took to deliver the equivalent of normal saline nebulized for 10 min in a PARI LC STAR(®) nebulizer to that of an investigational PARI eFlow(®). METHODS: Six normal adults inhaled a 4-mL (36-mg) charge volume of saline from the LC STAR(®) or a 2.5-mL (22.5-mg) charge volume from the investigational eFlow(®). The saline was mixed with (99m)Tc-DTPA to allow two-dimensional imaging. The inhalation was preceded by a xenon equilibration scan to allow more accurate separation of deposition into central and peripheral lung regions. RESULTS: The investigational eFlow(®) delivered 8.6 ± 1.0 mg, approximately 90% of the lung dose compared to the LC STAR(®), 9.6 ± 1.0 mg, but did in less than half the time (p < 0.02 for both). There were no differences in central versus peripheral distribution for either device. CONCLUSIONS: In conclusion the investigational eFlow(®) was both faster and more efficient than the LC STAR(®).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".