Effects of hip joint rotation on the trochanteric force and soft tissue thickness during sideways falls
Bibliographic record
Abstract
We measured the trochanteric soft tissue thickness (TSTT) to determine the force magnitude delivered to the proximal femur ( F trochanteric ), and examined how the TSTT and F trochanteric were affected by hip rotation during sideways falls. Twenty individuals participated in pelvis release experiments. Trials were acquired with three hip rotations: 15° external, 0° (neutral), and 15° internal. During trials, kinetics and kinematics of the pelvis and lower extremities were recorded. Outcome variables included the effective stiffness of the pelvis ( k ), F trochanteric , TSTT, and force magnitude attenuated by the TSTT ( F attenuation ). The k and F trochanteric were associated with hip rotation ( F = 5.06, p = 0.011; F = 5.49, p = 0.008, respectively). Both outcome variables were 14 % and 15 % smaller in external compared to neutral and internal rotation, respectively (45.3 versus 52.8 kN/m; 5448 versus 6425 N). However, neither the TSTT nor the F attenuation was associated with hip rotation ( F = 2.92, p = 0.066; F = 2.30, p = 0.114, respectively). The F trochanteric decreased with hip external rotation at impact. This was attributed to decreased pelvis stiffness rather than enhanced protective benefits (i.e., force attenuation) provided by the trochanteric soft tissue. These findings offer valuable insights into the distribution of hip impact forces and may contribute to a better understanding of hip impact biomechanics during falls.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".