FEM Simulation of Non-Progressive Growth from Asymmetric Loading and Vicious Cycle Theory: Scoliosis Study Proof of Concept
Bibliographic record
Abstract
Scoliosis affects about 1-3% of the adolescent population, with 80% of cases being idiopathic. There is currently a lack of understanding regarding the biomechanics of scoliosis, current treatment methods can be further improved with a greater understanding of scoliosis growth patterns. The objective of this study is to develop a finite element model that can respond to loads in a similar fashion as current spine biomechanics models and apply it to scoliosis growth. Using CT images of a non-scoliotic individual, a finite element model of the L3-L4 vertebra was created. By applying asymmetric loading in accordance to the 'vicious cycle' theory and through the use of a growth modulation equation it is possible to determine the amount of growth each region of the vertebra will undergo; therefore predict scoliosis growth over a period of time. This study seeks to demonstrate how improved anatomy can expand researchers current knowledge of scoliosis.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".