Higher Tobramycin concentration and vibrating mesh technology can shorten antibiotic treatment time in cystic fibrosis
Bibliographic record
Abstract
Poor adherence to recommended therapy in cystic fibrosis (CF) is often because of the time demands of therapy. Tobramycin (TOBI®, 300 mg at 60 mg/ml) inhaled from the PARI LC PLUS® nebulizer requires about 20 min. This study determined if equivalent levels of pulmonary deposition could be achieved in shorter time using 1.5 ml of 100 mg/ml tobramycin solution delivered by an investigational eFlow® nebulizer. Sixteen males with stable CF, 8 children and 8 adults, and an FEV(1) > 45% predicted inhaled both preparations on two occasions with (99m) Tc-DTPA added to the tobramycin. Blood samples were taken for quantification of tobramycin in the serum. The PARI LC PLUS® delivered 45.4 (39.3-51.6), mean and 95% CI, mg to the lungs in 17.0 ± 2.5 min (mean ± SD) with serum levels of 1,089 ± 388 µg/L. The investigational eFlow® delivered 46.3(40.3-51.7) mg in 4.0 ± 1.0 min with blood levels of 909 ± 458 µg/L. Only the time of delivery was significantly different with P < 0.0001 (paired t-test). Tolerability of the treatment was comparable for both inhalation regimes, but the shorter treatment was preferred by all patients. These results demonstrate the possibility of delivering equivalent levels of tobramycin much faster into the lungs of CF patients when using eFlow®, a very efficient electronic nebulizer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".