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Record W1973314455 · doi:10.1080/10739680600618710

The Microcirculation of Skeletal Muscle in Aging

2006· review· en· W1973314455 on OpenAlexaff
Geoffrey W. Payne, Shawn E. Bearden

Bibliographic record

VenueMicrocirculation · 2006
Typereview
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMicrocirculationSkeletal muscleBlood flowPerfusionAnatomyHemodynamicsMedicineNeuroscienceBiologyInternal medicine

Abstract

fetched live from OpenAlex

Microvascular structure and function are key aspects of tissue and organ health. At approximately 40% of total body mass, skeletal muscle contains more microvessels than any other organ system in the body. Moreover, skeletal muscle is the most dynamic tissue in the body with the capacity to increase blood flow and metabolic rate 30- to 50- fold. Aging is associated with decrements in microvascular function and exercise tolerance that are poorly understood. Here, experts in their respective fields of microvascular structure and function are brought together to review the current state of knowledge regarding microvascular adaptations to aging. Reviews are drawn from human and animal studies and focus on age-related changes in sympathetic nervous system control of microvessels, capillary hemodynamics and oxygen pressure, microvascular network structure and functional integration, microvascular reactivity, whole muscle perfusion, and cellular contacts and inflammation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.029
GPT teacher head0.316
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2006
Admission routes1
Has abstractyes

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