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Record W2736277953 · doi:10.1038/s41598-017-06349-y

The Effect of Electrical Stimulation of the Calf Muscle on Leg Fluid Accumulation over a Long Period of Sitting

2017· article· en· W2736277953 on OpenAlexaff
Daniel Vena, Jonathan Rubianto, Miloš R. Popović, Geoff Fernie, Azadeh Yadollahi

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSupine positionBioelectrical impedance analysisMedicineSittingStimulationEdemaBody fluidElectrical muscle stimulationLeg muscleFluid intakeAnesthesiaPhysical therapyPhysical medicine and rehabilitationInternal medicineBody mass indexPathology

Abstract

fetched live from OpenAlex

Leg fluid accumulation during sedentary behaviours such as sitting can lead to leg edema and associated adverse health consequences. This study investigates the use calf muscle electrical stimulation (ES) to reduce seated leg fluid accumulation. Thirteen non-obese, normotensive men (mean age 51 yr.) with sleep apnea were enrolled in the study. Participants first lay supine for 30 minutes to equalize fluid distribution and then sat for 150 minutes. While seated, participants received either active or sham ES of the calf muscles, according to random assignment. Participants returned one-week later to cross over to the other study condition. Leg fluid was measured continuously while sitting using the bioelectrical impedance method. Fluid accumulation in the leg was reduced by more than 40% using active ES, compared to sham ES (∆ = 51.9 ± 8.8 ml vs. ∆ = 91.5 ± 8.9 ml, P < 0.001). In summary, calf muscle ES is an effective method for reducing accumulation of fluid during long sedentary periods and has potential use as a device for preventing leg edema to treat associated health consequences in at-risk groups and settings.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.028
GPT teacher head0.336
Teacher spread0.307 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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