Factors that influence soft tissue thickness over the greater trochanter: Application to understanding hip fractures
Bibliographic record
Abstract
Fall-related hip injuries are a concern for the growing population of older adults. Evidence suggests that soft tissue overlying the greater trochanter attenuates the forces transmitted to the proximal femur during an impact, reducing mechanical risk of hip fracture. However, there is limited information about the factors that influence trochanteric soft tissue thickness. The current study used ultrasonography and electromyography to determine whether trochanteric soft tissue thickness could be quantified reproducibly and whether it was influenced by: (1) gender; (2) hip postures associated with potential falling configurations in the sagittal plane (from 30° of extension to 60° of flexion, at 15° intervals), combined adduction-flexion, and combined adduction-extension; and (3) activation levels of the tensor fascia lata (TFL) and gluteus medius (GM) muscles. Our results demonstrated that soft tissue thickness can be measured reliably in nine hip postures and three muscle activation conditions (for all conditions, ICC >0.98). Mean (SD) thickness in quiet stance was 2.52 cm. Thickness was 27.0% lower for males than females during quiet stance. It was 16.4% greater at maximum flexion than quiet standing, 27.2% greater at maximum extension, and 12.5% greater during combined adduction-flexion. However, there was no significant difference between combined adduction-extension and quiet standing. Thickness was not affected by changes in muscle activity. Forces applied to the femoral neck during a lateral fall decrease as trochanteric soft tissue thickness increases; gender and postural configuration at impact could influence the loads applied to the proximal femur (and thus hip fracture risk) during falls on the hip.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".