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Record W4388014344 · doi:10.1002/jcsm.13353

D3‐creatine dilution, computed tomography and dual‐energy X‐ray absorptiometry for assessing myopenia and physical function in colon cancer: A cross‐sectional study

2023· article· en· W4388014344 on OpenAlexaff
En Cheng, Bette J. Caan, Peggy M. Cawthon, William J. Evans, Marc K. Hellerstein, Mahalakshmi Shankaran, Kristin L. Campbell, Alexandra M. Binder, Barbara Sternfeld, Jeffrey A. Meyerhardt, Kathryn H. Schmitz, Elizabeth M. Cespedes Feliciano

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteNational Institutes of Health
KeywordsDual energyCreatineMedicineComputed tomographyDual-energy X-ray absorptiometryColorectal cancerTomographyCancerNuclear medicineRadiologyInternal medicineBone mineral

Abstract

fetched live from OpenAlex

BACKGROUND: Low skeletal muscle mass (myopenia) is common in cancer populations and is associated with functional decline and mortality, but prior oncology studies did not assess total body skeletal muscle mass. Instead, they measured surrogates such as cross-sectional area (CSA) of skeletal muscle at L3 from computed tomography (CT) or appendicular lean mass (ALM) from dual-energy X-ray absorptiometry (DXA). D3-creatine (D3Cr) dilution is a non-invasive method to assess total body skeletal muscle mass, which has been examined in a variety of populations but not in cancer. To compare the associations of D3Cr muscle mass, CT CSA, and DXA ALM with myopenia and physical function, we conducted a cross-sectional study among 119 patients with colon cancer (2018-2022). METHODS: For each technique (D3Cr, CT and DXA), myopenia was defined as the lowest sex-specific quartile of its measurement. Physical function was measured by the short physical performance battery and grip strength. We calculated Pearson correlations (r) among three techniques, computed Cohen's kappa coefficients (κ) to assess the agreement of myopenia, and estimated Pearson correlations (r) of three techniques with physical function. All analyses were sex-specific. RESULTS: Sixty-one (51.3%) participants were male, the mean (standard deviation) age was 56.6 (12.9) years, and most (68.9%) had high physical function (short physical performance battery: ≥11 points). Correlations and myopenia agreement among three techniques were greater in men than women; for example, regarding D3Cr muscle mass versus CT CSA, r was 0.73 (P < 0.001) for men versus 0.45 (P < 0.001) for women, and κ was 0.82 (95% CI: 0.65, 0.99) for men versus 0.24 (95% CI: -0.08, 0.52) for women. Among men, higher D3Cr muscle mass was significantly correlated with faster gait speed (r = 0.43, P < 0.01) and stronger grip strength (r = 0.32, P < 0.05); similar correlations were observed for CT CSA and DXA ALM. However, among women, no measure of muscle or lean mass was significantly associated with physical function. CONCLUSIONS: This is the first study using D3-creatine dilution method to assess muscle mass in a cancer population. Regardless of the techniques used for muscle or lean mass assessment, we observed stronger correlations, greater myopenia agreement, and more significant associations with physical function in men with colon cancer than women. D3Cr, CT and DXA are not interchangeable methods for assessing myopenia and physical function, especially in women with colon cancer. Future studies should consider relative advantages of these techniques and examine the D3-creatine dilution method in other cancer types.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.374
Teacher spread0.334 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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