Peripheral quantitative computed tomography-derived muscle density and peripheral magnetic resonance imaging-derived muscle adiposity: precision and associations with fragility fractures in women.
Bibliographic record
Abstract
PURPOSE: To determine the degree to which muscle density and fractures are explained by inter and intramuscular fat (IMF). METHODS: Women ⋝50 years of age (Hamilton, ON, Canada) had peripheral magnetic resonance imaging and peripheral quantitative computed tomography scans at 66% of the tibial length. Muscle on computed tomography images was segmented from subcutaneous fat and bone using fixed thresholds, computing muscle density. IMF was segmented from muscle within magnetic resonance images using a region-growing algorithm, computing IMF volume. Fracture history over the last 14 years was obtained. Odds ratios for fractures were determined for muscle density, adjusting for IMF volume, total hip BMD, age and body mass index. RESULTS: Women with a history of fractures were older (N=32, age:75.6±8.3 years) than those without (N=39, age: 67.0±5.2 years) (<0.01). IMF volume explained 49.3% of variance in muscle density (p<0.001). Odds for fractures were associated with lower muscle density even after adjusting for IMF volume but were attenuated after adjusting for age. CONCLUSIONS: Muscle adiposity represents only 50% of the muscle density measurement. Properties of muscle beyond its adiposity may be related to fractures, but larger and prospective studies are needed to confirm these associations.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".