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Cerebral Blood Volume and Blood Flow at Varying Arterial Carbon Dioxide Tension Levels in Rabbits During Propofol Anesthesia

2000· article· en· W1974966170 on OpenAlexaff
Aleksa Cenic, Rosemary A. Craen, Vicky L. Howard-Lech, Ting‐Yim Lee, Adrian W. Gelb

Bibliographic record

VenueAnesthesia & Analgesia · 2000
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsWestern UniversityLondon Health Sciences CentreLawson Health Research InstituteSt Joseph's Health Centre
Fundersnot available
KeywordsMedicinePropofolAnesthesiaCerebral blood flowCarbon dioxideBlood flowArterial bloodVolume (thermodynamics)Blood volumeCardiology

Abstract

fetched live from OpenAlex

UNLABELLED: There are little data on the effects of propofol on cerebral blood volume (CBV). We studied the effects of changes in PaCO(2) on CBV and cerebral blood flow (CBF) during propofol anesthesia in eight New Zealand white rabbits. We also investigated the effects of propofol over time on CBV and CBF during normocapnia (control group). At normocapnia, the mean (+/- SD) CBV and CBF values were 2.41 +/- 0.68 mL/100 g and 56 +/- 28 mL/100 g/min, respectively,. When PaCO(2) was reduced from 41 to 27 mm Hg, no significant change in either CBV or CBF was observed (P > 0.10). However, increasing PaCO(2) from 41 to 58 mm Hg resulted in a 30% increase in CBV (3.08 +/- 0.86 mL/100 g, P < 0.05) and a 91% increase in CBF (97 +/- 39 mL/100 g/min, P < 0.01). In the control group, there were no significant changes in CBV and CBF (P > 0.10) during 2 h of propofol anesthesia. These results indicate that, during propofol anesthesia, cerebrovascular reactivity of blood flow and blood volume is maintained during hypercapnia but is markedly diminished during hypocapnia. IMPLICATIONS: During propofol anesthesia in rabbits with normal brains, a reduction in the arterial carbon dioxide level may not always be accompanied by a reduction in brain blood flow and blood volume.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.012
GPT teacher head0.216
Teacher spread0.203 · 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.

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

Citations41
Published2000
Admission routes1
Has abstractyes

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