Biomechanical Evaluation of Osteoporotic Proximal Periprosthetic Femur Fractures With Proximal Bicortical Fixation and Allograft Struts
Bibliographic record
Abstract
OBJECTIVES: To evaluate the strength of proximal bicortical fixation using a novel osteoporotic synthetic bone model of Vancouver B1 periprosthetic proximal periprosthetic femur fractures (PFFs) and to assess the influence of strut allograft augmentation with regard to allowing early assisted weight bearing. The secondary aim was to evaluate whether the strut position, either medial or anterior, influenced the strength of the construct. METHODS: Thirty synthetic osteoporotic femurs were implanted with cemented stems. A segmental defect made distal to the stem simulated a fracture and was repaired with a stainless steel locking compression plate and 2 stainless steel proximal locking attachment plates. Specimens were then divided into 3 groups: no-strut, medial strut, and anterior strut. Cadaveric femoral struts were wired to the specimens. Cyclic axial compression simulated assisted weight bearing and was followed by loading to failure. RESULTS: Medial struts required higher failure load than no-strut (P = 0.008) and more energy to failure than anterior (P = 0.018) or no-strut (P < 0.001). The higher load to failure, however, would not be advantageous in clinical practice because estimates for assisted weight bearing after fractures in average-weight patients are well below these failure loads. Furthermore, all specimens tolerated cyclical loading. All failures occurred distal to the plate originating at the last screw hole. CONCLUSIONS: Failure loads for all groups were above what would be expected for low-demand activities of assisted weight bearing. Therefore, proximal bicortical fixation should allow for early, assisted weight bearing without allograft strut augmentation even with lower density 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 | 0.000 | 0.001 |
| 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".