Health Care Workers and Respiratory Protection: Is the User Seal Check a Surrogate for Respirator Fit-Testing?
Bibliographic record
Abstract
Many agencies recommend that health care workers wear N95 filtering facepiece respirators (N95-FFR) to minimize occupational exposure to bioaerosols, such as tuberculosis and pandemic influenza. Published standards outline procedures for the proper selection of an N95-FFR model, including user seal checks and respirator fit-testing. Some health officials have argued that the respirator fit-test step should be eliminated altogether, given its additional time and cost factors, and that only a user seal check be utilized to ensure that an adequate face seal has been achieved. One of the aims of the current study is to examine whether a user seal check is an appropriate surrogate for respirator fit-testing. Subjects were assigned an N95-FFR and asked to perform a user seal check (as per manufacturer's instructions) after which they immediately underwent a respirator fit-test. Successfully passing a respirator fit-test was based on not detecting a leakage through the face seal (either qualitatively with a test agent or quantitatively with a particulate counter). The sample population consisted of 647 subjects who had never been previously fit-tested (naive), while the remaining 137 participants were experienced respirator users. Only four of the 647 naive subjects (0.62%) identified an inadequate seal during their user seal check. Of the 643 remaining naive subjects who indicated that they had an adequate face seal prior to fit-testing, 158 (25%) failed the subsequent quantitative fit-test and 92 (14%) failed the qualitative fit-test. All 137 experienced users indicated that they had an adequate seal after performing the user seal check; however, 41 (30%) failed the subsequent quantitative fit-test, and 30 (22%) failed the qualitative fit-test. These findings contradict the argument to eliminate fit-testing and rely strictly on a user seal check to evaluate face seal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".