Error of Displacement Measurements using Digital Volume Correlation and High Resolution Peripheral Quantitative Computed Tomography
Bibliographic record
Abstract
Background: Digital volume correlation (DVC) is a 3D image processing technique for non-invasive assessment of internal deformation of structures in unloaded and loaded (deformed) states. One application of DVC is experimental validation of displacement predictions from subject-specific finite element (FE) models of bone. FE models can help clinicians and researchers better understand musculoskeletal diseases which affect bone mechanics (e.g., osteoporosis, osteoarthritis); however, FE models must be validated by experimental testing. Micro-CT is typically used for DVC due to its low noise and high resolution but the scanner is limited to small bone samples. High-resolution peripheral quantitative CT (HR-pQCT) can scan larger volumes, making it suitable for studying long bones affected by musculoskeletal disease (e.g., tibia, femur). The aim of this study was to estimate errors associated with DVC measures of displacement using HR-pQCT scans.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".