Development of image-based beam model for assessment of osteoporotic hip fracture risk
Bibliographic record
Abstract
Hip fracture has been identified as a major worldwide health problem among the elderly population. A fast, accurate and effective evaluation of hip fracture risk is essential for accurate health care planning and selecting a proper treatment. Therefore, the high applicability and the universal availability are required for assessing a technique. The objective of this study was to develop a two-dimensional subject-specific beam model, which is easy to be adopted into a clinical environment to assess hip fracture risk. First, the equivalence between CTXA (computed tomography X-ray absorptiometry) and QCT (quantitative computed tomography) derived femur cross-section stiffness was studied. Then, the CTXA-based femur cross-section stiffness was used in the beam model to calculate the hip fracture risk index (FRI) during sideways fall and single-leg stance loading configuration. Finally, the test of discrimination between PPI (proton-pump inhibitor) users and non-PPI users based on cross-sectional stiffness, BMD (bone mineral density) and FRI was conducted to demonstrate if PPI use is associated with the presence of osteoporosis or accelerated BMD loss. Strong correlation is found between CTXA and QCT derived femur cross-section stiffness, which indicates that QCT can be replaced by CTXA in assessing femur bone quality. Therefore, DXA can be a replacement of QCT to calculate femur cross-sectional properties due to the equivalence between CTXA and DXA derived mechanical properties. It is also demonstrated that the cross-sectional stiffness, BMD, and FRI cannot discriminate the PPI users from non-PPI users, which means that there is no difference between PPI users and non-PPI users in cross-sectional stiffness, BMD, and FRI. This may suggested that PPI use is not associated with the presence of osteoporosis or accelerated BMD loss. The proposed beam model can be easily adopted into clinic to predict hip fracture risk, and this beam model derived FRI can be used in some clinical verification. Yet its accuracy of discriminate fracture will be investigated in a future study.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".