Clinical Utility of Thigh and Mid‐Thigh Dual‐Energy x‐Ray Absorptiometry to Identify Bone and Muscle Loss
Bibliographic record
Abstract
Abstract Sarcopenia and osteoporosis are highly prevalent syndromes in older people, characterized by loss of muscle and bone tissue, and related to adverse outcomes. Previous reports indicate mid‐thigh dual‐energy X‐ray absorptiometry (DXA) is well suited for the simultaneous assessment of bone, muscle, and fat mass in a single scan. Using cross‐sectional clinical data and whole‐body DXA images of 1322 community‐dwelling adults from the Geelong Osteoporosis Study (57% women, median age 59 years), bone and lean mass were quantified in three unconventional regions of interest (ROIs): (i) a 2.6‐cm‐thick slice of mid‐thigh, (ii) a 13‐cm‐thick slice of mid‐thigh, and (iii) the whole thigh. Conventional indices of tissue mass were also calculated (appendicular lean mass [ALM] and bone mineral density [BMD] of lumbar spine, hip, and femoral neck). The performance of thigh ROIs in identifying osteoporosis, osteopenia, low lean mass and strength, past falls, and fractures was evaluated. All thigh regions (especially whole thigh) performed well in identifying osteoporosis (area under the receiver‐operating characteristic [ROC] curve [AUC] > 0.8) and low lean mass (AUC >0.95), but they performed worse in the diagnosis of osteopenia (AUC 0.7–0.8). All thigh regions were equivalent to ALM in discrimination of poor handgrip strength, gait speed, past falls, and fractures. BMD in conventional regions was more strongly associated with past fractures than thigh ROIs. In addition to being faster and easier to quantify, mid‐thigh tissue masses can be used for identifying osteoporosis and low lean mass. They are also equivalent to conventional ROIs in their associations with muscle performance, past falls, and fractures; however, further validation is required for the prediction of fractures. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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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 | 0.003 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".