Biomechanical Evaluation for Mechanisms of Periprosthetic Femoral Fractures
Bibliographic record
Abstract
BACKGROUND: There are a number of biomechanical tests for various treatment options of periprosthetic femoral fractures, but different loading modalities prelude their direct comparison. This study was designed to develop an experimental model of osteoporotic bone fractures near the femoral stem that is based on a simple testing protocol to increase the reproducibility. In addition, we wanted to clarify whether a femoral prosthetic stem reduces the femoral fracture strength. METHODS: Twenty human cadaveric femurs were harvested, and five groups were randomized on the basis of the bone mineral density using a pQCT device. The specimens of three groups were provided with a cemented Exeter V40 stem and loaded to failure with torsion (I), anterior (II), and lateral load (III). The femurs of groups IV and V remained uninstrumented and were tested in a four-point bending assay similar to groups II and III. All biomechanical testings were realized with a servohydraulic testing machine (MTS). RESULTS: There was no significant difference regarding bone mineral density of all groups. Torsional testing generated proximal intertrochanteric fractures and anterior loading resulted exclusively in supracondylar fractures. Introducing the force from the lateral side, all fracture lines occurred close to the tip of the stem, similar to a Vancouver-B fracture. Assuming that lateral load application is a main responsible mechanism of periprosthetic femoral fracture near the tip of the stem, the fracture strength of instrumented femurs was significantly reduced (group III: 4,692 N vs. group V: 6,931 N; p < 0.05). CONCLUSION: Prosthetic stems reduce femoral fracture strength significantly. In an osteoporotic bone model, a four-point bending test with lateral load application seems to be a suitable approach.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".