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Record W2062499735 · doi:10.1177/021849230601400611

Effect of pH Management on Brain Perfusion during Retrograde Cerebral Perfusion

2006· article· en· W2062499735 on OpenAlexaff
Yanmin Yang, Zhijun Li, Luojia Yang, Michael Jackson, Allan Turner, Jian Ye

Bibliographic record

VenueAsian Cardiovascular and Thoracic Annals · 2006
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsNational Research Council Institute for BiodiagnosticsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePerfusionPerfusion scanningCerebral perfusion pressureCardiologyInternal medicine

Abstract

fetched live from OpenAlex

This study was undertaken to determine the effects of different pH management strategies during retrograde cerebral perfusion on the relationship between retrograde perfusion pressure and brain tissue perfusion. Six pigs were subjected to an alpha-stat strategy and another 6 to a pH-stat strategy during hypothermic (15 degrees C) retrograde cerebral perfusion at perfusion pressures of 10 to 70 mm Hg, in increments of 10 mm Hg every 20 min. Regional cerebral blood flow was significantly higher in the pH-stat group than in the alpha-stat group. The cerebral blood flow peaked at perfusion pressures of 40-50 mm Hg (18.6% +/- 10.8% in the pH-stat group vs. 3.6% +/- 1.2% in the alpha-stat group). In both groups, the intracranial pressure remained below the critical level of 25 mm Hg, even at a retrograde perfusion pressure of 70 mm Hg. Cerebral lactate production was higher in the alpha-stat group than the pH-stat group during retrograde cerebral perfusion at pressures of 10-30 mm Hg. Compared to the alpha-stat strategy, the pH-stat strategy significantly improved brain tissue perfusion. With an open inferior vena cava, the optimal perfusion pressure seems to be 40-50 mm Hg.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.278
Teacher spread0.266 · 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 designOther design
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

Citations2
Published2006
Admission routes1
Has abstractyes

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