Prediction of impact force in sideways fall by image-based subject-specific dynamics model
Bibliographic record
Abstract
The impact force applied to the greater trochanter during sideways fall is a critical factor for determining whether or not a hip fracture would occur. However, the impact force is subject-dependent as it is related to the subject’s anthropometric parameters and the kinematic variables in fall. It cannot be accurately predicted by the currently available dynamics models. We developed and validated a method for constructing subject-specific dynamics models to more accurately predict the impact force. The anthropometric parameters required in the model were obtained from the subject’s whole body DXA (dual energy X-ray absorptiometry) image. The subject-specific dynamics models were then validated by protected fall tests using young volunteers. The effects of anthropometric parameters on the impact force were investigated using 90 clinical DXA images obtained from a local osteoporosis clinic center. The impact forces predicted by subject-specific dynamics models had much better agreement with the experimental data, compared with those predicted by the existing empirical functions. The parametric study results indicated that although body weight and height are the dominant parameters affecting the impact force, other parameters such as the hip vertical velocity before impact also have considerable effects. This finding suggests that the existing empirical functions that only consider body weight and height may not be able to accurately predict the impact force. As whole body DXA images are readily available in osteoporosis clinic centers, the proposed method may have potential applications in the clinic to improve the assessment of fall-induced 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".