Design of Aerosol Face Masks for Children Using Computerized 3D Face Analysis
Bibliographic record
Abstract
BACKGROUND: Aerosol masks were originally developed for adults and downsized for children. Overall fit to minimize dead space and a tight seal are problematic, because children's faces undergo rapid and marked topographic and internal anthropometric changes in their first few months/years of life. Facial three-dimensional (3D) anthropometric data were used to design an optimized pediatric mask. METHODS: Children's faces (n=271, aged 1 month to 4 years) were scanned with 3D technology. Data for the distance from the bridge of the nose to the tip of the chin (H) and the width of the mouth opening (W) were used to categorize the scans into "small," "medium," and "large" "clusters." RESULTS: "Average" masks were developed from each cluster to provide an optimal seal with minimal dead space. The resulting computerized contour, W and H, were used to develop the SootherMask® that enables children, "suckling" on their own pacifier, to keep the mask on their face, mainly by means of subatmospheric pressure. The relatively wide and flexible rim of the mask accommodates variations in facial size within and between clusters. CONCLUSIONS: Unique pediatric face masks were developed based on anthropometric data obtained through computerized 3D face analysis. These masks follow facial contours and gently seal to the child's face, and thus may minimize aerosol leakage and dead space.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".