Performing a Three-Dimensional Finite Element Analysis to Simulate and Quantify the Contact Pressure in the Canine Elbow Joint: A Pilot Study
Bibliographic record
Abstract
Abstract Objective The aim of this study was to measure surface pressures and force distribution on radius and ulna in healthy and dysplastic elbow joints in different positions using the finite element analysis (FEA). Study Design FEA was performed on computed tomographic data of healthy and fragmented coronoid process diseased elbow joints of Labrador Retrievers. It considered the articular cartilage, collateral ligaments, triceps and biceps muscle. The analysis of each joint was performed in four positions (standing position: 145 degrees and three positions of the stance phase of gait: beginning: 115 degrees, middle: 110 degrees, end: 145 degrees joint angle) in consideration of different ground reaction forces (standing: 88.3 N; stance phase of gait: 182.5 N). Results Mean values of total force of 317.5 N (standing), 590.7 N (beginning), 330.9 N (middle) and 730.9 N (end) were measured. The percentual force distribution resulted in a total of 49.56 ± 26.58% on the ulna with a very inhomogeneous distribution. A significant difference was detected between the positions ‘standing’ and ‘end’ (p = 0.0497) regardless of the joint condition. In some FEA results, visual assessment of the surface pressures indicated an increase in pressure in the region of the medial compartment without a uniform pattern. An increase in pressure resulted in an area increase in the pressure marks on the joint surface and measurable pressure was increased at a larger joint angle. Clinical Significance FEA can provide information about the transmission of force in the joint. Prior to the use of FEA in scientific clinical research for the simulation of force, further model improvements are necessary.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".