The path to developing realistic finite element long bone models
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
Current computational capabilities allow for rapid construction of finite element (FE) models but do not guarantee representative models. Simplifications to FE models are necessary because of computational limitations and scarcity of physiological data. With proper modelling and validation, FE models can progress from the realm of parametric studies to clinical applicability. It is often unclear, in the preliminary stages of FE model development, what simplifications are suitable without sacrificing solution accuracy and clinical relevance. This paper presents a technique to create proper FE long bone models for those wanting to develop their own studies. It highlights four important parameters (geometry, material properties, loading conditions, validation) that must be carefully considered and presents a number of methods to aid in achieving proper representation of each parameter. Knee bones are used as an example but the technique can be extended to reconstruct different long bones in the human body with some adjustments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it