Realistic 3D CT-FEM for Target-based Multiple Organ Inclusive Studies
Bibliographic record
Abstract
Computed Tomography-based Finite Element Model (CT-FEM) is a powerful tool that enables the collaboration of clinicians and engineers in biomechanics.It allows accurate and efficient simulations to improve understanding of complex biological problems.Despite its potential benefits, computational biomechanics using CT-FEM faces several challenges when dealing with complex geometries.To address this challenge, an advanced methodology is here developed by using four different software simultaneously.The software can work together and supply user interaction to complete the segmentation, surface reduction, surface mesh generation, and acoustic analysis.One of the most challenging geometries, the human thorax with multiple internal organs, was chosen to test the methodology.The approach has been validated against two different and independent experimental studies available in the literature.It could be used to offer insights into the effects on the multiple internal organs in many clinical and therapeutic studies.This specific approach allows researchers to explore complex interactions happening inside the human body, resulting in major advancements in comprehending physiological and pathological procedures.
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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 | 0.001 |
| 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".