Assessing head muscles measured with MRI as alternative to estimate muscle mass in older persons with dementia
Bibliographic record
Abstract
Abstract Background Current evidence suggests an association between sarcopenia and multiple negative outcomes. Traditional methods to diagnose sarcopenia are based on dual‐energy X‐ray absorptiometry (DXA) and whole body magnetic resonance imaging. These tests are complicated, time‐consuming and expensive. We aim to bring a more accessible way to diagnose sarcopenia, making its detection easier in a neurological and dementia setting where brain MRI are regularly ordered. Therefore, we aim to compare the traditional methods of muscle mass and function vs. muscle quantification using the tongue and the masseter muscle in a standard brain MRI to diagnose sarcopenia in clinical settings. Method The H70 study, is a longitudinal study of people born in the Gothenburg area of Sweden aged 70 at baseline. We included 791 participants with available clinical data and MRI. DXA and bioimpedance analyses were used as reference measures. Images were analyzed using 3Dslicer.(figure 1) Adjusted speermarRo coefficients were calculated to assess the correlation between the gold standard (DXA) and the muscle mass of the tongue and masseters. Result There was a higher prevalence of women (55,7%). When comparing the different tests to assess muscle mass, we found significant and positive coefficient between both tongue and masseter. Total lean tissue mass (DXA) & Tongue volume (Cm3) Rho = 0,375, total lean tissue mass (DXA) & Total masseter volume (Cm3) Rho = 0,326. < 0.001. (table1,2 Figure 2.) Conclusion There is a significant positive correlation between total lean tissue mass using DXA and muscle mass calculated with MRI measuring tongue and masseter. Clinical longitudinal Implications of these measurements will be tested in further steps of this research and exposed at the conference.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".