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Record W2792318209 · doi:10.1093/jcag/gwy008.322

A321 USING BEDSIDE ULTRASOUND AS A TOOL TO DETECT SARCOPENIA FOR CIRRHOTIC PATIENTS ON TRANSPLANT LIST

2018· article· en· W2792318209 on OpenAlexaff
Mohammad Ag. Alfawaz, Lynn Sinclair, M Hilwah, B M Aljudaibi, Paul Marotta, Karim Qumosani

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineLiver transplantationSarcopeniaLiver diseaseUltrasoundMalnutritionTransplantationCohortAtrophyProspective cohort studyMuscle atrophySkeletal muscleInternal medicinePhysical therapyRadiology

Abstract

fetched live from OpenAlex

Muscle atrophy is present in as many as 40% of cirrhotic patients and associated with increased morbidity and mortality in those awaiting liver transplantation. A two-fold increase in mortality when compared to non-sarcopenic patients occurs independent of liver dysfunction evaluated using Model for End-Stage Liver Disease (MELD) score which does not incorporate markers of nutritional status, or muscle loss. Ultrasound offers the possibility of a non-invasive and affordable method to evaluate skeletal muscle at the bedside. It has been validated and is emerging as a valuable prognostic indicator of muscle atrophy, thereby improving detection of malnutrition at the individual level. We aim to evaluate quadriceps muscle layer thickness (QMLT) using ultrasound in cirrhotic patients waiting for liver transplantation across a range of nutritional risk scores based on Royal Free Hospital Nutrition Prioritizing Tool (RFNS). QMLT will also be compared to functional measures such as hand-grip and blood tests. A prospective study started in July 2016 using QMLT measures in a cohort of adult patients waiting for liver transplantation. Written informed consent is obtained on an individual bases. Measures of QMLT are obtained using the ultrasound probe at frequency 9 htz for each thigh at the middle and two-third point from superior iliac spine (SIS). Two residents who received expert training conduct measures. These measures will be compared to a nutrition score as well as synthetic markers of malnutrition, and functional measures of strength and endurance. Ten patients have been recruited so far. The average QMLT measured at two-thirds from SIS is 3 cm on both legs. The average thickness at the mid-point is 4.2 cm for both legs. Those patients considered at severe risk of malnutrition based on RFNS had either lower, or close to average QMLT when compared to the entire group. Two of the severely malnourished patients had a higher than average measurement which may reflect significant lower-leg edema. Serum vitamin A, and D levels were low in 6 patients and none of those had a higher than average QMLT. Average NaMELD score was 21 in 4 patients with low QMLT. Interestingly, the high RFNS patients had lower NaMELD scores. Hand grip measures on two patients were considered low for both of those who also had low QMLT. Results were consistent between both residents. Based on this prospective study, QMLT may offer and valuable and objective prognostic tool for detection of sarcopenia and high nutritional risk with consistent results. Significant lower leg edema may present a limitation of this tool. Future work is needed to optimize the ability of QMLT and to determine its importance in assessing the role of lean body mass in cirrhotic populations. None

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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