Biomechanical Analysis Using FEA and Experiments of Metal Plate and Bone Strut Repair of a Femur Midshaft Segmental Defect
Bibliographic record
Abstract
This investigation assessed the biomechanical performance of the metal plate and bone strut technique for fixing recalcitrant nonunions of femur midshaft segmental defects, which has not been systematically done before. A finite element (FE) model was developed and then validated by experiments with the femur in 15 deg of adduction at a subclinical hip force of 1 kN. Then, FE analysis was done with the femur in 15 deg of adduction at a hip force of 3 kN representing about 4 x body weight for a 75 kg person to examine clinically relevant cases, such as an intact femur plus 8 different combinations of a lateral metal plate of fixed length, a medial bone strut of varying length, and varying numbers and locations of screws to secure the plate and strut around a midshaft defect. Using the traditional "high stiffness" femur-implant construct criterion, the repair technique using both a lateral plate and a medial strut fixed with the maximum possible number of screws would be the most desirable since it had the highest stiffness (1948 N/mm); moreover, this produced a peak femur cortical Von Mises stress (92 MPa) which was below the ultimate tensile strength of cortical bone. Conversely, using the more modern "low stiffness" femur-implant construct criterion, the repair technique using only a lateral plate but no medial strut provided the lowest stiffness (606 N/mm), which could potentially permit more in-line interfragmentary motion (i.e., perpendicular to the fracture gap, but in the direction of the femur shaft long axis) to enhance callus formation for secondary-type fracture healing; however, this also generated a peak femur cortical Von Mises stress (171 MPa) which was above the ultimate tensile strength of cortical bone.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".