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Record W61223676

Changes in superficial blood distribution in thigh muscle during LBNP assessed by NIRS.

2004· article· en· W61223676 on OpenAlexaff
Tesshin Hachiya, Andrew P. Blaber, Mitsuru Saito

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBlood volumeThighOxygenationVenous bloodChemistryMedicineInternal medicineAnesthesiaAnatomy
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The present study was designed to determine how superficial blood distributed in the lower limb muscle during graded lower body negative pressure (LBNP). METHODS: Near-infrared spectroscopy (NIRS) was used to evaluate the blood volume change in the thigh muscles of seven volunteers during 35 min graded LBNP (rest, -10, -20, -30, -40, -50 mm Hg, and recovery). RESULTS: Deoxygenated and total hemoglobin (Hb) increased in proportion to the magnitude of LBNP applied to the thigh muscles. Oxygenated Hb rose significantly at -10 mm Hg LBNP, although the increase leveled off during subsequent increments of LBNP. Systolic pressure significantly decreased from 120 mm Hg at rest, to a value of 108 at -50 mm Hg LBNP. In contrast, mean and diastolic pressures were well maintained during graded LBNP. The increased total and deoxygenated Hb might indicate that blood was held in venous space, and the magnitude of rise in blood volume corresponded to the change in LBNP. On the other hand, oxygenated Hb change seems to reflect mainly blood accumulated in arterial space by interacting between mechanical stretch induced by LBNP and vasoconstriction caused by augmented sympathetic nerve activity. CONCLUSION: From these results, blood distribution in thigh muscles was different and was affected by the strength of LBNP. The data assessing oxygenation sites of Hb were found to be useful as indices of estimating superficial blood pooling in the muscle during LBNP.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.207
Teacher spread0.197 · 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

Citations15
Published2004
Admission routes1
Has abstractyes

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