Drug delivery from a Metered Dose Inhaler (MDI) / Spacer delivering triple therapy and self reported observations from COPD patients following the introduction of a Spacer
Bibliographic record
Abstract
Objective: This lab study aimed to assess the drug delivery of a relatively new COPD triple therapy MDI with spacer, and seek feedback from COPD patients who were using MDIs with a specific spacer. Methods: Cascade Impactor testing was performed using Trimbow* beclomethasone dipropionate/formoterol fumarate/glycopyrronium bromide (BD/FF/GB 100/6/12.5 mcg/actuation) pMDI, delivered 0s after actuation, simulating perfect but unlikely coordination. A more realistic 2s delay (to simulate misuse) was investigated for MDI with Spacer (AeroChamber Plus* Flow-Vu* VHC). Patient feedback was received from 437 COPD patients in a voluntary UK/Canada database (MyAero*) comparing the main differences they experienced when using the Spacer with a pMDI compared to pMDI alone. Results: Fine Particle Mass (µg/actuation; mean+/-sd) and Fine Particle Fraction (%; mean+/-sd): erj;66/suppl_69/PA350/TB1 T1 TB1 pMDI alone pMDI / Spacer Delay (s) 0 (coordinated) 2 (uncoordinated) FPMBD 47.8 +/-0.8 44.7 +/-5.9 FPMFF 2.4 +/-0.2 2.3 +/-0.5 FPMGB 5.8 +/-0.1 5.7 +/-0.9 FPFBD 51.5 +/-2.3 96.4 +/-0.4 FPFFF 49.6 +/-3.6 100.0 +/-0.0 FPFGB 50.5 +/-2.0 94.1 +/-1.5 79% of surveyed patients reported more confidence in medication delivery, 10% reported fewer emergency visits, and 10% reported less side effects. Conclusions: These results suggest that the Spacer tested can enable effective pMDI drug delivery, providing consistent delivery of the intended dose (even with poor coordination) and maximize fine particle delivery. The COPD patients surveyed provided positive feedback relating to confidence in medication delivery and some also reported less emergency visits / side effects.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".