Facial Anthropometric Dimensions of Koreans and Their Associations with Fit of Quarter-Mask Respirators.
Bibliographic record
Abstract
Past studies on respirator fit or performance have mostly been done for Whites or male subjects, and little attention has been paid to minorities and Asians. To fill this gap, this study was designed to provide facial anthropometric data for Koreans and to analyze the association between facial dimensions and respirator fit factors for three brands of quarter-mask respirators, two domestic and one imported brand, using a Portacount 8020. A total of 110 university student subjects, 70 males and 40 females volunteered for participation in the study. The results of this study showed that Korean males and females have different facial dimensions as compared with those of White males and females. Unexpectedly, the imported respirator performed better than the domestic respirators. Males were found to achieve better respirator fit than females regardless of respirator brands tested. The regression analysis found no common prognostic variables with the three respirator brands studied. A stepwise logistic regression analysis was conducted to find predictive facial dimensions with respirator fits. Some facial dimensions were found to be statistically significant, but these dimensions are different from the traditionally recommended facial dimensions of face length and lip width for quarter mask. To improve respirator fit for Koreans, these different facial characteristics need to be considered in the design of quarter mask respirators.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".