Biomechanical analysis of fracture risk associated with tibia deformity in children with osteogenesis imperfecta: a finite element analysis.
Bibliographic record
Abstract
OBJECTIVES: Osteogenesis imperfecta (OI) frequently leads to long-bone bowing requiring a surgical intervention in severe cases to avoid subsequent fractures. However, there are no objective criteria to decide when to perform such intervention. The objective is to develop a finite element model to predict the risk of tibial fracture associated with tibia deformity in patients with OI. METHODS: A comprehensive FE model of the tibia was adapted to match bi-planar radiographs of a 7 year-old girl with OI. Ten additional models with different deformed geometries (from 2° to 24°) were created and the elasto-plastic mechanical properties were adapted to reflect OI conditions. Loads were obtained from mechanography of two-legged hopping. Two additional impact cases (lateral and torsion) were also simulated. Principal strain levels were used to define a risk criterion. RESULTS: Fracture risks for the two-legged hopping load case remained low and constant until tibia bowing reached 15° and 16° in sagittal and coronal planes respectively. Fracture risks for lateral and torsion impact were equivalent whatever the level of tibial bowing. CONCLUSIONS: The finite element model of OI tibia provides an objective means of assessing the necessity of surgical intervention for a given level of tibia bowing in OI-affected children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".