The biomechanics of plate fixation of periprosthetic femoral fractures near the tip of a total hip implant: cables, screws, or both?
Bibliographic record
Abstract
Femoral shaft fractures after total hip arthroplasty (THA) remain a serious problem, since there is no optimal surgical repair method. Virtually all studies that examined surgical repair methods have done so clinically or experimentally. The present study assessed injury patterns computationally by developing three-dimensional (3D) finite element (FE) models that were validated experimentally. The investigation evaluated three different constructs for the fixation of Vancouver B1 periprosthetic femoral shaft fractures following THA. Experimentally, three bone plate repair methods were applied to a synthetic femur with a 5 mm fracture gap near the tip of a total hip implant. Repair methods were identical distal to the fracture gap, but used cables only (construct A), screws only (construct B), or cables plus screws (construct C) proximal to the fracture gap. Specimens were oriented in 15 degrees adduction to simulate the single-legged stance phase of walking, subjected to 1000 N of axial force, and instrumented with strain gauges. Computationally, a linearly elastic and isotropic 3D FE model was developed to mimic experiments. Results showed excellent agreement between experimental and FE strains, yielding a Pearson linearity coefficient, R2, of 0.92 and a slope for the line of best data fit of 1.06. FE-computed axial stiffnesses were 768 N/mm (construct A), 1023 N/mm (construct B), and 1102 N/mm (construct C). FE surfaces stress maps for cortical bone showed Von Mises stresses, excluding peaks, of 0-8 MPa (construct A), 0-15 MPa (construct B), and 0-20 MPa (construct C). Cables absorbed the majority of load, followed by the plates and then the screws. Construct A yielded peak stress at one of the empty holes in the plate. Constructs B and C had similar bone stress patterns, and can achieve optimal fixation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".