Biomechanical analysis using infrared thermography of a traditional metal plate versus a carbon fibre/epoxy plate for Vancouver B1 femur fractures
Bibliographic record
Abstract
Traditional high-stiffness metal plates for Vancouver B1 femur shaft fractures below the tip of a hip implant can cause stress shielding, bone resorption, and implant loosening. This is the first study to compare the biomechanics of a traditional metal plate versus a low-stiffness carbon fibre/epoxy composite plate for this injury. A total hip replacement was implanted in two previously validated intact artificial femurs. Femurs were fitted with either a metal or composite plate and had a 5 mm fracture gap created to simulate a Vancouver B1 shaft fracture. Femurs were cyclically loaded using 5 Hz at 7° of adduction with an average axial load of 800 N (range = 400-1200 N). Overall mechanical stiffnesses and femur and plate thermographic stresses were obtained. Femur/metal plate stiffness (698 N/mm) was only 12% higher than femur/composite plate stiffness (625 N/mm). The femur with the composite plate had 22.7% higher combined average stress compared to the femur with the metal plate, having specific differences of 29.5% (anterior view), 33.9% (posterior view), 1.0% (medial view), and 26.4% (lateral view). The composite plate itself had an average 21.1% reduction in stress compared to the metal plate. The composite plate reduced stress shielding, yet provided adequate stiffness.
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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 | 0.000 |
| 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 | 0.000 | 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".