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Record W2000705331 · doi:10.1139/h04-009

Perfused Skeletal Muscle-An Experimental Preparation for Many Questions¡

2004· review· en· W2000705331 on OpenAlexaff
J. K. Barclay, W. N. Stainsby

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSkeletal muscleContext (archaeology)MetaboliteStimulationIn vivoPerfusionMetabolismBlood flowAutoregulationBiologyInternal medicineChemistryBiochemistryAnatomyEndocrinologyMedicineBlood pressureBiotechnology

Abstract

fetched live from OpenAlex

Perfused mammalian skeletal muscle preparations either in vitro or in situ are one of the options to be considered when planning a physiological research program or project. Such preparations have been and continue to be used to investigate research questions as diverse as skeletal muscle function and metabolism, peripheral vascular function, and an approximation of exercise. When selecting a perfused muscle preparation, both anatomical and physiological organization must be evaluated in the context of the planned experiment. In any experiment, a number of physiologically significant variables can be manipulated, such as the level of flow and the arterial or inflow concentration of a gas or substance to control substrate supply and metabolite removal as well as the stimulation parameters to alter metabolic rate. The choice of blood or an artificial perfusate is of paramount importance because, when compared to blood-perfused preparations, those receiving artificial perfusates show depressed vascular autoregulation among other changes, indicating a decrease in physiological quality. Overall, perfused skeletal muscle preparations can be used to examine many and varied research questions with close to in-vivo quality and a high degree of accuracy and control if blood-perfused.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
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 head0.020
GPT teacher head0.303
Teacher spread0.284 · 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 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

Citations2
Published2004
Admission routes1
Has abstractyes

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