Estimating Muscle Mass Using D3-Creatine Dilution: A Narrative Review of Clinical Implications and Comparison With Other Methods
Bibliographic record
Abstract
BACKGROUND: The D3-creatine (D3-Cr) dilution method is of emerging interest for estimating total-body skeletal muscle mass. This review explores the association of muscle mass estimated via D3-Cr with various clinical outcomes and provides a summary of the literature comparing D3-Cr with other body composition techniques. METHODS: A literature search was conducted on PubMed/MEDLINE and Web of Science for studies using D3-Cr to measure muscle in adult populations (ie, ≥18 years old) from inception until September 2023. RESULTS: Out of the 23 included studies, 15 investigated the correlation between D3-Cr and clinical outcomes. More consistent associations were reported for mortality (100%, n = 2), mobility disability (100%; n = 5), falls and fractures (100%; n = 3), physical performance (63.3%; n = 11), muscle strength (44.4%; n = 9), and muscle composition (33.3%; n = 3). However, conflicting findings were also reported for such correlations. Among the 23 studies, 14 compared D3-Cr-estimated muscle with other body composition techniques, including magnetic resonance imaging (MRI) as a reference method. Strong and positive correlations were found between D3-Cr and MRI. Nonetheless, variations in muscle measurements were noted, with differences in D3-Cr values ranging from 0.62 kg lower to 13.47 kg higher compared to MRI. CONCLUSIONS: D3-Cr-estimated muscle mass may be a valuable predictor of clinical outcomes showing consistent associations with falls and fractures, mobility disability, and mortality. However, less consistent associations were found with muscle strength and composition, and physical performance. Although a strong correlation exists between D3-Cr-estimated muscle mass and MRI measurements, under- or overestimation may occur.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".