Magnetic resonance imaging based finite element modelling of the proximal femur: a short-term in vivo precision study
Bibliographic record
Abstract
Proximal femoral fractures are a serious life-threatening injury with high morbidity and mortality. Magnetic resonance (MR) imaging has potential to non-invasively assess proximal femoral bone strength in vivo through usage of finite element (FE) modelling (a technique referred to as MR-FE). To precisely assess bone strength, knowledge of measurement error associated with different MR-FE outcomes is needed. The objective of this study was to characterize the short-term in vivo precision errors of MR-FE outcomes (e.g., stress, strain, failure loads) of the proximal femur for fall and stance loading configurations using 13 participants (5 males and 8 females; median age: 27 years, range: 21-68), each scanned 3 times. MR-FE models were generated, and mean von Mises stress and strain as well as principal stress and strain were calculated for 3 regions of interest. Similarly, we calculated the failure loads to cause 5% of contiguous elements to fail according to the von Mises yield, Brittle Coulomb-Mohr, normal principal, and Hoffman stress and strain criteria. Precision (root-mean squared coefficient of variation) of the MR-FE outcomes ranged from 3.3% to 11.8% for stress and strain-based mechanical outcomes, and 5.8% to 9.0% for failure loads. These results provide evidence that MR-FE outcomes are a promising non-invasive technique for monitoring femoral strength in vivo.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".