An Experimental Setup for API Assessment of a Valved Holding Chamber Device
Bibliographic record
Abstract
Typically for young children and elders, the asthma treatment procedure using pMDI devices bring some usage coordination difficulties. Therefore, and accordingly to several asthma treatment guidelines, the prescription of a VHC, as an add-on device for the pMDI, is advisable. These devices consist of an expansion chamber where the air slows down, as well as, the pMDI spray plume. Allowing the patient to breathe whenever he wants, independently from the moment of the pMDI actuation, also reduces the “cold-freon” effect and allows a more effective evaporation of the propellant. The effectiveness evaluation of VHC and pMDI devices is made through the quantification of drug delivered to the patient lungs. A simple collection filter is not enough for an accurate assessment of the device. Since the size of the particles delivered matter the most, the use of an impaction apparatus is essential. Accordingly to the Canadian normative for VHC assessment (CAN/CSA/Z264.1-02:2008), the experimental testing shall be done by using a breath simulator instead of a constant flow pump. The evolution of these tests shall move towards more realistic testing conditions. This work reports the project and construction an experimental setup for a correct assessment of the VHC devices effectiveness. The experimental setup is based in the work of Foss & Keppel (1999) and the contribution of Finlay (1998) and Miller (2002). The project and optimization of the major components, such as, breath cycle simulator by means of a cam-follower mechanism, a mixing cone and the vacuum pump used, are herein described and discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".