The effect of image reconstruction kernel and density modulus relationship in finite element models of simulated cadaveric acromial loading
Bibliographic record
Abstract
Fractures of the acromion are a common complication following surgical procedures of the shoulder due to changes in joint biomechanics. To improve understanding of acromial stresses and evaluate surgical procedures, image-based finite element models (FEMs) may be used. Image-based FEMs are dependent on accurate volumetric bone mineral density (vBMD), as this relates to mechanical properties in FEMs. Image reconstruction kernel alters vBMD; however, the effect on FEM output during simulated loading of the acromion has not been reported. The objective of this study was to compare predicted forces from FEMs derived from two common kernels and four density-modulus relationships to experimental forces in cadaveric scapulae (n = 10). Scapular FEMs were generated from CT scans reconstructed using bone sharpening (BONE) and standard (STD) kernels using four density-modulus relationships. Displacements were applied corresponding to experimental data collected on cadaveric specimens and forces were compared for each FEM. Specimen-specific percentage errors were as low as 1 % when using BONE kernel vBMD as input. Across all FEMs, the most accurate density-modulus relationship had a lower mean absolute percentage error (40 %) compared to the other three relationships compared (275 %, 281 %, 547 %), which greatly overestimated experimental forces. Across all models, those derived with STD kernel vBMD (40 %) had lower mean absolute percentage error relative to BONE kernel vBMD (42 %). This study highlights the relative accuracy of current density-modulus relationships using vBMD from two common reconstruction kernels. More accurate density-modulus relationships that account for variations in kernel parameters are required for FEM estimates of acromial forces and fracture predictions. Current models are not able to replicate experimental forces in cadaveric scapulae.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".