Investigation of repeatability in hip fracture risk predicted by DXA-based finite element model
Bibliographic record
Abstract
DXA (dual energy X-ray absorptiometry) based finite element model is able to integrate all mechanical factors affecting hip fracture in osteoporosis patients and it is thus, in principle, more reliable than areal bone mineral density (BMD) for assessing fracture risk. However, short-term repeatability of DXA-based finite element model in predicting fracture risk has not yet been investigated and satisfactory repeatability is a prerequisite for the procedure to be applied in clinic. Therefore, in the reported research, the repeatability of a previously developed DXA-based patient-specific finite element procedure was investigated. It was found that inconsistence in positioning the patient during DXA scanning and manual segmentation of DXA image in constructing the finite element model are the two dominant factors affecting short-term repeatability of the finite element procedure. The study outcome indicated that to apply the finite element procedure in clinic, a set of more strict guidelines for positioning the patient in DXA scanning must be established and followed.
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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 | 0.004 | 0.024 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".