Optimisation of locking plate fixation methods for periprosthetic fractures
Bibliographic record
Abstract
Periprosthetic fracture (PPF) of the Femur is a common complication of hip arthroplasty. With increasing rates of total hip replacements, the occurrence of periprosthetic fractures is expected to rise. These fractures are often challenging to treat effectively due the technical challenges presented with the combination of fractured bone and an unstable prosthesis. Failure of locking plate fixation of fractures around the tip of a stable prosthesis (Vancouver type B1) have been reported clinically, suggesting that further investigation into their treatment is needed. \nThis study developed a computational periprosthetic fracture fixation model, using experimentally tested specimens to validate the model. Clear relationships could be identified between the experimental and computational results for the Intact Femur, total hip replacement (THR) and PPF cases. The model could predict the magnitude of the strain in the plate and hence the likelihood of plate fracture, as well as assessing the relative stiffness of different fixation scenarios. The model was suitable for the identification and prediction of changes in strain and stiffness between a set of comparative cases and was used to comment on their relative biomechanical performances. \nThe angle of a periprosthetic fracture was shown to have a significant effect on stabilised construct mechanics and specifically, the direction of the fracture has a very large effect on fracture stabilisation. Fractures in the ML direction were less stable than fractures in the LM direction. The 45° Medial to Lateral fracture case was the least stabile and the instrumentation configuration used in this study is clearly not optimal for this fracture case. It is recommended that the orientation of the fracture should be taken into account by surgeons when deciding on B1 PPF management.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".