The Effects of Wearing a 3‐Ply or <scp>KN95</scp> Face Mask on Cerebral Blood Flow and Oxygenation
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 virus has impacted life in many ways, one change being the use of face masks. Their effect on MRI-based measurements of cerebral oxygen levels with quantitative susceptibility mapping (QSM) and cerebral blood flow (CBF) is not known. PURPOSE: This study investigated whether wearing a face mask leads to changes in CBF and cerebral venous oxygen saturation measured with MRI. STUDY TYPE: Repeated-measures cohort study. POPULATION: A total of 16 healthy volunteers (eight male, eight female; 22-36 years) were recruited for the 3-ply study. Ten of the 16 participants (five male, five female; 23-36 years) took part in the KN95 study. FIELD STRENGTH/SEQUENCE: A 3 T, single-delay 3D gradient-and spin-echo pseudo-continuous arterial spin labeling (pCASL) scan for CBF quantification, and gradient-echo for QSM and oxygenation quantification. ASSESSMENT: -weighted gray matter tissue segmentation. Venous oxygenation was determined from venous segmentation of QSM maximum intensity projections. STATISTICAL TESTS: Paired Student's t-tests and Cohen's d effect sizes were used to compare the face mask and no face mask scans for gray matter CBF, gray matter magnetic susceptibility, and cerebral venous oxygen saturation. Standard t-tests were used to assess whether the order of scanning with and without a mask had any impact. A statistical cut off of P < 0.05 was used. RESULTS: The 3-ply masks increased gray matter CBF from an average of 43.99 mL/(100 g*min) to 46.81 mL/(100 g*min). There were no significant changes in gray matter magnetic susceptibility (P = 0.07), or cerebral venous oxygen saturation (P = 0.36) for the 3-ply data set. The KN95 masks data set showed no statistically significant changes in gray matter CBF (P = 0.52) and magnetic susceptibility (P = 0.97), or cerebral venous oxygen saturation (P = 0.93). DATA CONCLUSION: The changes in blood flow and oxygenation due to face masks are small. Only CBF increased significantly due to wearing a 3-ply mask. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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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) | 0.000 | 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) | 0.003 | 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".