Accuracy and precision of internal displacement and strain measurements in long human bones using HR-pQCT and digital volume correlation
Bibliographic record
Abstract
Digital volume correlation (DVC) is a technique for measuring 3D, internal displacements and strains in loaded structures.One application of DVC is the study of internal bone mechanics and the validation of subject-specific finite element (FE) models.While micro-computed tomography (µCT) is a common choice for DVC studies of small samples of bone, long human bones such as the tibia may exceed the spatial limitations of conventional µCT.High resolution peripheral quantitative CT (HR-pQCT) scanners, with their large bore and open-ended design, may be a viable option for long-bone DVC.However, HR-pQCT is afflicted by stitching artefacts, caused by the stacking of scan blocks to form the large scan volume, resulting in erroneous steps in DVC displacements and bands of increased strain error.This study proposed a modified HR-pQCT scanning protocol and stitching methodology to mitigate the effects of stitching artefacts on DVC measurements of displacement and strain.With the application of the proposed scanning/stitching methodology, displacements greater than 11.7µm (0.29 voxels) and strains greater than 1633µε could be repeatedly measured by DVC.These results show that HR-pQCT combined with DVC is suitable for measuring internal bone displacements in long human bones with sub-voxel precision and strains greater than 1633µε.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".