Evaluation of mesh morphing and mapping techniques in patient specific modeling of the human pelvis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Robust generation of pelvic finite element models is necessary to understand the variation in mechanical behaviour resulting from differences in gender, aging, disease and injury. The objective of this study was to apply and evaluate mesh morphing and mapping techniques to facilitate the creation and structural analysis of specimen-specific finite element (FE) models of the pelvis. A specimen-specific pelvic FE model (source mesh) was generated following a traditional user-intensive meshing scheme. The source mesh was morphed onto a computed tomography scan generated target surface of a second pelvis using a landmarked-based approach, in which exterior source nodes were shifted to target surface vertices, while constrained along a normal. A second copy of the morphed model was further refined through mesh mapping, in which surface nodes of the initial morphed model were selected in patches and remapped onto the surfaces of the target model. Computed tomography intensity based material properties were assigned to each model. The source, target, morphed and mapped models were analyzed under axial compression using linear static FE analysis and their strain distributions evaluated. Morphing and mapping techniques were effectively applied to generate good quality geometrically complex specimen-specific pelvic FE models. Mapping significantly improved strain concurrence with the target pelvis FE model.
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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 | 0.003 | 0.001 |
| 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 it