Computerized Dead-Space Volume Measurement of Face Masks Applied to Simulated Faces
Bibliographic record
Abstract
BACKGROUND: The dead-space volume (VD) of face masks for metered-dose inhaler treatments is particularly important in infants and young children with asthma, who have relatively low tidal volumes. Data about VD have been traditionally obtained from water displacement measurements, in which masks are held against a flat surface. Because, in real life, masks are placed against the face, VD is likely to differ considerably between masks depending upon their contour and fit. The aim of this study was to develop an accurate and reliable way to measure VD electronically and to apply this technique by comparing the electronic VD of commonly available face masks. METHODS: Average digital faces were obtained from 3-dimensional images of 270 infants and children. Commonly used face masks (small and medium) from various manufacturers (Monaghan Medical, Pari Respiratory Equipment, Philips Respironics, and InspiRx) were scanned and digitized by means of computed tomography. Each mask was electronically applied to its respective digital face, and the VD enclosed (mL) was computerized and precisely measured. RESULTS: VD varied between 22.6 mL (SootherMask, InspiRx) and 43.1 mL (Vortex, Pari) for small masks and between 41.7 mL (SootherMask) and 71.5 mL (AeroChamber, Monaghan Medical) for medium masks. These values were significantly lower and less variable than measurements obtained by water displacement. CONCLUSIONS: Computerized techniques provide an innovative and relatively simple way of accurately measuring the VD of face masks applied to digital faces. As determined by computerized measurement using average-size virtual faces, the InspiRx masks had a significantly smaller VD for both small and medium masks compared with the other masks. This is of considerable importance with respect to aerosol dose and delivery time, particularly in young children. (ClinicalTrials.gov registration NCT01274299.).
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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 | 0.001 | 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) | 0.002 | 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".