Precision study of DXA‐based patient‐specific finite element modeling for assessing hip fracture risk
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Bibliographic record
Abstract
Finite element (FE) modeling based on a patient's hip dual energy X-ray absorptiometry (DXA) image is a promising tool for more accurately assessing hip fracture risk, as it is able to comprehensively consider effects from all the mechanical parameters affecting hip fracture. However, a number of factors influence the precision (also known as repeatability or reproducibility) of a DXA-based FE procedure, for example, subject positioning in DXA scanning. As a procedure is required to have adequately high precision in clinical application, we investigated the effects of the involved factors on the precision of a DXA-based patient-specific FE procedure developed by the authors, to provide insight into how the precision of the procedure can be improved so that it can meet the clinical standards. Fracture risk indices corresponding to initial and repeat DXA scans acquired in 30 typical clinical subjects were computed and compared to assess short term repeatability of the procedure. It was found that inconsistent positioning followed by manual segmentation of the projected femur contour induced significant variability in the predicted fracture risk indices. This research suggests that, to apply the DXA-based FE procedure in clinical assessment, it will be necessary to pay more strict attention to subject positioning in DXA scanning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 it