Addressing the Main Barrier to Sarcopenia Identification: Utility of Practical Office-Based Bioimpedance Tools Vs. Dual Energy X-ray Absorptiometry (DXA) Body Composition for Identification of Low Muscle Mass in Older Adults
Bibliographic record
Abstract
Background: Sarcopenia is associated with increased morbidity and mortality. Clinically, sarcopenia can be overlooked, especially in obesity. Sarcopenia diagnostic criteria include muscle mass (MM) and function assessments. Muscle function can be readily assessed in a clinic setting (grip strength, chair stand test). However, MM requires dual-energy X-ray absorptiometry (DXA) Body Composition (BC) or other costly tools, not readily available. Methods: Full Body Sensor, Shiokoji Horikawa, Kyoto, Japan] to DXA. The OMRON differs from the Ozeri scale because the OMRON also includes hand sensors. The European Working Group on Sarcopenia in Older People (EWGSOP) DXA or BIA low MM diagnostic cut-offs were used to classify participants as having low or normal MM. Results: ], 7 with Ozeri, and 27 with OMRON, had low MM. Positive predictive value for low MM versus DXA (as the gold standard) for Ozeri was 73.3% and OMRON was 92.8%. Good correlation between BIA scales and DXA for body fat estimates. Conclusions: OMRON captured all low MM participants identified by DXA plus all on DXA diagnostic borderline. Prevalence of obesity was high. Clinically, sarcopenic obese is the most difficult phenotype, as obesity masks low muscle mass. Low cost, readily available, direct-to-consumer BIA BC scales, especially with hand sensors, provide immediate, reliable information on muscle and fat mass. This can prompt appropriate investigation and/or intervention for sarcopenia or sarcopenic obesity.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".