Intra-rater reliability of B-mode ultrasound in the measurement of soft tissue thickness and composition over the lateral proximal femur
Bibliographic record
Abstract
Comprehensive characterization of soft tissue composition and distribution over the lateral proximal femur is necessary to improve our understanding of fall-related hip fractures; however, a protocol that produces reliable data is needed. Accordingly, the aim of this study was to assess the intra-rater reliability of B-mode ultrasound measured muscle, adipose, and total soft tissue thicknesses over the lateral proximal femur in a simulated sideways fall configuration. Twenty-five young adults (12 males, 13 females, age (SD) = 20.8 (1.9) years) underwent B-mode ultrasound imaging of 12 locations over their lateral proximal femur while in a side-lying position. Side-lying was chosen to allow imaging of the tissues impacted during a lateral fall event. Muscle, adipose, and total soft tissue thicknesses were measured at each location (further grouped into posterolateral, lateral, and anterolateral locations for statistical analysis) and two-way mixed model absolute agreement intraclass correlation coefficients (ICCs) and standard error of measurement were used to assess reliability of tissue type, location, and sex. ICCs were excellent (ICC >0.9) for each tissue type, measurement location, and sex. There were interactions between tissue type, measurement location, and sex on reliability. While measures of adipose thickness were excellent across all conditions, reliability of muscle and total soft tissue thickness measurements were dependent on measurement location and sex. This ultrasound imaging protocol demonstrated good-to-excellent reliability of soft tissue-specific thickness measurements over the lateral proximal femur. This protocol has implications for subject-specific hip fracture risk screening tools and biomechanical models of impact dynamics that incorporate biofidelic soft tissue morphology.
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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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".