Compatible Stability and Aerosol Characteristics of Atrovent® (Ipratropium Bromide) Mixed with Salbutamol Sulfate, Terbutaline Sulfate, Budesonide, and Acetylcysteine
Bibliographic record
Abstract
(1) Background: It is common practice in the treatment of respiratory diseases to mix different inhalation solutions for simultaneous inhalation. At present, a small number of studies have been published that evaluate the physicochemical compatibility and aerosol characteristics of different inhalation medications. However, none of them studied Atrovent®. Our work aims to address the lack of studies on Atrovent®. (2) Methods: Portions of admixtures were withdrawn at certain time intervals after mixing and were tested by pH determination, osmolarity measurement, and high-performance liquid chromatography (HPLC) assay of each active ingredient as measures of physicochemical compatibility. The geometrical and aerosol particle size distribution, active drug delivery rate, and total active drug delivered were measured to characterize aerosol behaviors. (3) Results: During the testing time, no significant variation was found in the pH value, the osmotic pressure, or the active components of admixtures. With the increase in nebulization volume after mixing, fine particle dose (FPD) and total active drug delivered showed statistically significant improvements, while the active drug delivery rate decreased compared to the single-drug preparations. (4) Conclusions: These results endorse the physicochemical compatibility of Atrovent® over 1 h when mixed with other inhalation medications. Considering aerosol characteristics, simultaneous inhalation is more efficient.
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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".