A New Method to Generate a Patient-Specific Finite Element Model of the Human Buttocks
Bibliographic record
Abstract
Finite element (FE) models are very efficient tools to study internal stresses in human structures that induce severe pressure sores. Unfortunately, methods currently used to generate FE models are not suitable for clinical application involving wheelchair users. A clinical-oriented method, based on calibrated-biplanar radiographs, was therefore developed to generate a subject-specific FE model of the buttocks in a non-weighted sitting position. The model was then used to analyze the stress distribution within the buttocks and compare two wheelchair seat cushions designs. Additional radiographs and pressure measurements in a weighted sitting position were acquired to validate the FE model experimentally. Results from the FE model were in good agreement with experimental data and related literature. An internal peak pressure of 45.3 kPa was observed while seated on a flat foam cushion, corresponding to an interface pressure of 23.6 kPa. Both pressures occurred underneath the ischial tuberosities. When compared to the flat foam cushion, the contoured foam cushion reduced internal and interface peak pressures by 18% and 33%, respectively. The method developed in this study has a great potential for clinical use. The FE model, by predicting realistic stress distributions, allows for the selection of a convenient wheelchair seat cushion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".