A Compound Risk Indicator for Subject-Specific Prediction of Hip Fracture in Sideways Falls
Bibliographic record
Abstract
Low-trauma hip fracture, usually caused by fall from standing height, has become a main source of morbidity and mortality among the elderly. However, the hip fracture risk is subject-dependent as it is related to the subject’s anthropometric parameters, the kinematic/kinetic variables in fall, and the hip anatomy. A compound fracture risk indicator is proposed in this study to define the correlation between the hip fracture risk and subject’s parameters such as anthropometric parameters, bone mineral density (BMD), height of body mass center (HMC), and hip soft tissue thickness (STT). In this study, a two-level, subject-specific biomechanical model composed of a whole-body dynamics model and a proximal femur finite element model was used to predict the hip fracture risk for 80 subjects. The required information was obtained from the subject’s whole body and hip DXA (dual energy X-ray absorptiometry) images. Then, multivariate nonlinear least-square fits of power functions were used to investigate the effect of parameters on hip fracture risk. Results indicated that although BMD is the dominant parameter affecting the hip fracture risk, other parameters such as hip soft tissue thickness and anthropometric parameters also have considerable effects. This finding suggests that the hip fracture risk cannot be accurately predicted by the currently available single factor predictors as they do not consider all multifactorial parameters. However, the proposed function can properly predict the risk of hip fracture in the absence of any computational simulation and biomechanical model. It also quantifies the effect of musculoskeletal-, and organ-level parameters on hip fracture risk and demonstrates which individual is more likely to experience hip fracture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".