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Record W2297698096 · doi:10.1042/cs20150694

Cerebral blood flow regulation, exercise and pregnancy: why should we care?

2016· review· en· W2297698096 on OpenAlexafffund
Michèle Bisson, Isabelle Marc, Patrice Brassard

Bibliographic record

VenueClinical Science · 2016
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsCerebral blood flowPregnancyMedicineBlood flowPhysical medicine and rehabilitationPhysical therapyInternal medicineBiology

Abstract

fetched live from OpenAlex

Cerebral blood flow (CBF) regulation is an indicator of cerebrovascular health increasingly recognized as being influenced by physical activity. Although regular exercise is recommended during healthy pregnancy, the effects of exercise on CBF regulation during this critical period of important blood flow increase and redistribution remain incompletely understood. Moreover, only a few studies have evaluated the effects of human pregnancy on CBF regulation. The present work summarizes current knowledge on CBF regulation in humans at rest and during aerobic exercise in relation to healthy pregnancy. Important gaps in the literature are highlighted, emphasizing the need to conduct well-designed studies assessing cerebrovascular function before, during and after this crucial life period to evaluate the potential cerebrovascular risks and benefits of exercise during pregnancy.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.135
GPT teacher head0.411
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2016
Admission routes2
Has abstractyes

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