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Record W4411469040 · doi:10.14814/phy2.70402

<scp>N95</scp> masks increase brain blood velocity and parasympathetic outflow, yet worsen orthostatic symptoms in a healthy cohort

2025· article· en· W4411469040 on OpenAlexafffund
Tania J. Pereira, Heather Edgell

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthostatic vital signsMedicineOutflowCohortOrthostatic intoleranceInternal medicineCardiologyBlood pressurePhysics

Abstract

fetched live from OpenAlex

Abstract Mask wearing became commonplace in everyday life during the COVID‐19 pandemic and masks are frequently used in certain professions. Masks can increase end‐tidal CO 2 and the cerebrovasculature is known to vasodilate in response to hypercapnia. Orthostatic intolerance (OI) is the inability to tolerate postural transitions due to the displacement of blood volume away from the cerebral circulation, and we hypothesized that wearing a mask would improve OI symptoms by increasing brain blood flow. Young, healthy participants ( n = 27) completed 10 min of 70° head‐up tilt while wearing/not wearing an N95 mask (randomized), while hemodynamics and blood velocity within the middle cerebral artery (MCAv) were measured. Systolic, diastolic, and mean blood pressure and mean and diastolic MCAv decreased during tilt in both conditions (all p &lt; 0.05; Table 1). Systolic MCA V was elevated while wearing a mask ( p &lt; 0.05). Some OI symptoms were exacerbated during the mask trial (all p &lt; 0.05) including the overall OI score ( p = 0.002; Table 2). There appears to be a disconnect between the physiological response to mask wearing and OI symptoms, potentially indicating that there is a psychological component to mask wearing. While we have provided evidence that masking increases brain blood flow, the psychological effects may outweigh potential benefits to the cerebral circulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.265
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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