The Effects of Body Position on Trochanteric Soft Tissue Thickness—Implications for Predictions of Impact Force and Hip Fracture Risk During Lateral Falls
Bibliographic record
Abstract
Trochanteric soft tissue thickness (TSTT) is a protective factor against fall-related hip fractures. This study's objectives were to determine: (1) the influence of body posture on TSTT and (2) the downstream effects of TSTT on biomechanical model predictions of fall-related impact force (Ffemur) and hip fracture factor of risk. Ultrasound was used to measure TSTT in 45 community-dwelling older adults in standing, supine, and side-lying positions with hip rotation angles of -25°, 0°, and 25°. Supine TSTT (mean [SD] = 5.57 [2.8] cm) was 29% and 69% greater than in standing and side-lying positions, respectively. The Ffemur based on supine TSTT (3380 [2017] N) was 19% lower than the standing position (4173 [1764] N) and 31% lower than the side-lying position (4908 [1524] N). As factor of risk was directly influenced by Ffemur, the relative effects on fracture risk were similar. While less pronounced (<10%), the effects of hip rotation angle were consistent across TSTT, Ffemur, and factor of risk. Based on the sensitivity of impact models to TSTT, these results highlight the need for a standardized TSTT measurement approach. In addition, the consistent influence of hip rotation on TSTT (and downstream model predictions) support its importance as a factor that may influence fall-related hip fracture risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".