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Record W1964337575 · doi:10.1152/ajpheart.00761.2014

Heterogeneous patterns of vasoreactivity in the middle cerebral and internal carotid arteries

2015· article· en· W1964337575 on OpenAlexafffund
Nicole S. Coverdale, Sophie Lalande, Amanda Perrotta, J. Kevin Shoemaker

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsMiddle cerebral arteryHypocapniaHypercapniaInternal carotid arteryTranscranial DopplerHyperventilationMedicineCerebral arteriesCardiologyInternal medicineUltrasoundMagnetic resonance imagingCerebral blood flowAnesthesiaIschemiaRadiologyRespiratory system

Abstract

fetched live from OpenAlex

This study compared changes in cross-sectional area (CSA) and flow (Q) between the middle cerebral artery (MCA) and the internal carotid artery (ICA) at baseline and during 5 min of hypercapnia (HC; 6% CO2) and hypocapnia (HO; hyperventilation) and quantified how these changes contribute to estimates of cerebrovascular reactivity (CVR). Measures of MCA CSA were made using 3T magnetic resonance imaging. On a separate day, MCA flow velocity was measured with transcranial Doppler ultrasound and ICA diameters and flow velocity were measured with duplex ultrasound. Fourteen subjects (23 ± 3 yr, 7 females) participated, providing data for 11 subjects during HC and 9 subjects during HO. An increase in MCA CSA (P < 0.05) was observed within the first minute of HC. During HO, the decrease in MCA CSA (P < 0.05) was delayed until minute 4. No changes were observed in ICA CSA during HC or HO. The relative changes in QICA and QMCA were similar during HC and HO. Therefore, the MCA, but not ICA, dilates and constricts during 5 min of HC and HO, respectively. The consequent impact on QMCA significantly affects estimates of CVR, and reactivity cannot be attributed solely to changes in smaller arterioles.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.865
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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

Citations76
Published2015
Admission routes2
Has abstractyes

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