A preliminary dual-energy X-ray absorptiometry-based finite element model for assessing osteoporotic hip fracture risk
Bibliographic record
Abstract
To more accurately assess osteoporotic hip fracture risk in a specific patient, a dual-energy X-ray absorptiometry (DXA)-based finite element model was constructed from the patient's femur DXA image. The outermost contour of the femur bone segmented from the DXA image was used to generate a finite element mesh. Bone mechanical properties, such as Young's modulus, are correlated with areal bone mineral density (BMD) captured in the DXA image. A quasi-static loading condition representing a sideway fall was applied to the finite element model. Three fracture risk indices were introduced and expressed as ratios of internal forces caused by impact forces occurring in sideway fall to bone ultimate cross-section strength at the three critical locations, i.e. the femoral neck, the intertrochanteric region, and the subtrochanteric region. The proposed finite element modelling procedure was validated against six representative clinical cases extracted from the Manitoba BMD database, where initial and follow-up DXA images have been taken to monitor longitudinal variation of areal BMD in individual patients. It was found from the clinical validation that variations in the proposed fracture risk indices have the same trends as those indicated by the conventional areal BMD and T-score. In addition, by the three proposed fracture risk indices it is possible to further identify the specific fracture location. It was also found that for the same subject, the variations in the three fracture risk indices have quite different magnitudes, with intertrochanteric region the largest and subtrochanteric region the smallest, which is probably owing to the different content of trabecular and cortical bones in the three regions. With further development, it is promising that the proposed DXA-based finite element model will be a useful tool for accurate assessment of osteoporosis development and for treatment monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".