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Record W2400006723

A method for measuring systolic and diastolic microcirculatory red cell flux within the canine myocardium.

2001· article· en· W2400006723 on OpenAlexaff
Katherine D. Barclay, Gerald A. Klassen, René Wong, Adrian Y. C. Wong

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMicrocirculationLaser Doppler velocimetryDiastoleCardiologyBlood flowInternal medicineRed CellVelocimetryArteryMedicineBiomedical engineeringOpticsBlood pressurePhysics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Knowledge of the patterns of movement of red cells during the cardiac cycle in the microcirculation within the contracting myocardium is largely unknown. We describe a method of making such measurements in the canine myocardium using the technique of laser Doppler velocimetry. METHODS: A lensed 100 microm fiber-optic probe was inserted into the beating myocardium at various sites. Using an ultra-stable laser and achieving measurement stability by heterodyning the laser light and reflected light from the tissue, it was possible to obtain a stable high quality measurement of predominately red cell movement in the microcirculation. RESULTS: Unique regional patterns of red cell movement within the myocardium were observed. Epicardial flux was continuous with peaks while endocardial flux was predominately diastolic. Stopping flow in the epicardial artery for 5-6 s demonstrated that red cell movement continues in the microcirculation with some reduction followed by a delayed reactive hyperemia. Modeling demonstrates an important role for the small coronary veins in control of microcirculatory red cell movement. CONCLUSIONS: It is possible using laser Doppler velocimetry to measure red blood cell flux in the beating canine myocardium. Such measurements demonstrate a high degree of complexity which is not reflected in epicardial coronary arterial or venous flow.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.039
GPT teacher head0.231
Teacher spread0.191 · 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

Citations9
Published2001
Admission routes1
Has abstractyes

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