Evaluation of Different Spacers Available in Chile for Salbutamol Delivery in Children and Adults
Bibliographic record
Abstract
Introduction: The use of aerosol therapy is essential in the management of chronic respiratory diseases. The effectiveness of a metered-dose inhaler (MDI) is greater when used with a spacer. Our objective was to evaluate six commercially available spacers used in Chile's primary healthcare system. Methods: We included 7 spacers. Each spacer with a mask was connected to the anatomical model, with the airway linked to a breathing simulator (ASL5000) via a filter positioned at the outlet to capture drug particles reaching the carina. The breathing simulator was set to an adult model (VT=500 mL, I:E=1:2, RR=13 cycles/min) and a child model (VT=155 mL, I:E=1:2, RR=25 cycles/min). Five actuations of 100 µg Salbutamol were administered at 30-s intervals, collected from specific locations along the aerosol pathway, and analyzed using HPLC. Results: The drug particles captured by the filter were 18.1±4.8, 14.9±0.9, 27.8±2.3, and 41.6±5.0 for Inhalasynt, Aerofacidose, AAheal, and Aerochamber, respectively. For the pediatric spacers, the particles captured by the filter were 6.0±0.5, 2.0±0.2, 3.0±0.7, and 19.2±2.3 for AAheal, Allbriefs, Ultracega, and Aerochamber, respectively. Conclusion: Aerochamber had the highest drug deposition among all the spacers. Clinicians should be aware that not all spacers are the same and may deliver significantly different amounts of medication. erj;66/suppl_69/PA351/F1 F1 F1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".