MétaCan
Menu
Back to cohort
Record W2322384450 · doi:10.1097/mco.0000000000000088

Bedside ultrasound measurement of skeletal muscle

2014· review· en· W2322384450 on OpenAlexaff
Marina Mourtzakis, Paul E. Wischmeyer

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2014
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Waterloo
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineSkeletal muscleMuscle atrophyUltrasoundSarcopeniaAtrophyMuscle massIntensive care medicinePhysical medicine and rehabilitationPathologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Skeletal muscle and lean body mass may be vital to prognosis and functional recovery in chronic and acute illness, particularly in conditions in which muscle atrophy is prevalent. Ultrasound provides a precise and expedient method to measure muscle mass and changes in skeletal muscle at the bedside. RECENT FINDINGS: Here, we describe the various methodological approaches along with the validation and reliability tests that have been performed in various populations. Current applications of ultrasound in chronic and acute illness as well as its limitations and strengths in quantifying the muscle mass and changes in muscle over time are discussed. To capitalize on the beneficial features of ultrasound for measuring muscle, we describe the work that is needed to optimize the usefulness of ultrasound in chronic disease and acute care. SUMMARY: Given the precision, practicality, and ease of use, ultrasound is emerging as a highly useful tool in expediently measuring the muscle mass and changes in muscle tissue at the bedside. Ultrasound may be valuable in identifying patients who are at risk of malnutrition, in tracking muscle atrophy for the purpose of calculating nutrient delivery, and in assessing the success or failure of nutrition, pharmacological and rehabilitative interventions that aim to counter muscle atrophy.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.299
GPT teacher head0.523
Teacher spread0.224 · 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

Citations112
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Clinical Nutrition & Metabolic CareSame topicNutrition and Health in AgingFrench-language works237,207