The effect of onlay cortical fibula strut grafts on biomechanical features of Vancouver type B1 periprosthetic femoral fractures
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate biomechanically the effects of onlay fibula grafts on Vancouver Type B1 Periprosthetic Femoral Fractures (PPFs). METHODS: Vancouver Type B1 PFF models were created in 25 fourth-generation synthetic femurs and fixed with locking plates using bicortical, unicortical screws, and cables. While no graft was used in group 1, onlay fibula grafts were placed anteriorly in group 2 and medially in group 3. In group 4, the cortical strut allograft was placed on the medial femoral cortex, and a locking compression plate (LCP) was applied to the lateral femoral cortex. In group 5, the strut allograft was placed over the anterior cortex of the femur and fixed with the same technique as in group 4. All models were then subjected to rotational and axial cyclical stiffness tests and load to failure to measure and compare the mechanical strengths of the constructs. RESULTS: The mean stiffness values of group 4 with medial allograft, before and after cyclical loading, were higher than all other groups, under both rotational and axial forces. The mean stiffness values of fibula autografts (groups 2 and 3) were similar to that of anterior allografts (group 5) in each test except that the mean initial axial stiffness of group 5 was higher than group 2. Failure loads were also not different between the groups. CONCLUSION: Although the rigidity of Vancouver type B1 periprosthetic femur fractures is highest if allografts are placed medially, fibula autografts can also provide similar fixation strengths to allografts if locking plates with unicortical and bicortical screws and cables are used.
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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 | 0.000 |
| 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 | 0.000 | 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".