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Record W3123824594 · doi:10.1038/s41598-021-81837-w

Ventilatory response to hypercapnia is increased after 4 h of head down bed rest

2021· article· en· W3123824594 on OpenAlexafffund
Kevin R. Murray, Sara Wasef, Heather Edgell

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationYork University
KeywordsHypercapniaBed restRest (music)Head (geology)MedicineCardiologyInternal medicineBiologyRespiratory system

Abstract

fetched live from OpenAlex

Abstract Head-down bed rest (HDBR) has previously been shown to alter cerebrovascular and autonomic control. Previous work found that sustained HDBR (≥ 20 days) attenuates the hypercapnic ventilatory response (HCVR); however, little is known about shorter-term effects of HDBR nor the influence of HDBR on the hypoxic ventilatory response (HVR). We investigated the effect of 4-h HDBR on HCVR and HVR and hypothesized attenuated ventilatory responses due to greater carotid and brain blood flow. Cardiorespiratory responses of young men (n = 11) and women (n = 3) to 5% CO2 or 10% O2 before and after 4-h HDBR were examined. HDBR resulted in lower HR, lower cardiac output index, lower common carotid artery flow, higher SpO2, and higher pulse wave velocity. After HDBR, tidal volume and ventilation responses to 5% CO2 were enhanced (all P < 0.05), yet no other changes in cardiorespiratory variables were evident. There was no influence of HDBR on the cardiorespiratory responses to hypoxia (all P > 0.05). Short-duration HDBR does not alter the HVR, yet enhances the HCVR, which we hypothesize is a consequence of cephalic CO2 accumulation from cerebral congestion.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.013
GPT teacher head0.290
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes2
Has abstractyes

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