Periprosthetic Femur Fractures After Total Hip Arthroplasty: Risk Factors and Management Strategies
Bibliographic record
Abstract
Background: Periprosthetic femur fractures (PFFs) are increasingly recognized as a significant complication following total hip arthroplasty (THA), particularly as the number of procedures rises. While THA is one of the most successful orthopedic surgeries, PFFs can compromise outcomes and demand complex management. Objective: To review current literature and identify patient- and implant-related risk factors for PFFs, as well as to summarize effective management strategies guided by classification systems. Methods: A structured literature review was conducted using PubMed, focusing on articles published in the past 10 years. Eight peer-reviewed studies were selected based on relevance to femoral fractures post-THA. Data on incidence, risk factors, stem design, and treatment approaches were extracted and synthesized. Results: Key patient-related risk factors include advanced age, female sex, osteoporosis, low body mass index, and certain metabolic conditions. Implant-related risk is elevated with cementless stems, particularly collarless and single-wedge taper designs. The Vancouver Classification system effectively guides treatment: stable fractures (B1) are managed with fixation, while unstable fractures (B2/B3) often require stem revision with cables. High reoperation rates are associated with unstable fractures. Conclusion: Recognition of risk factors and individualized treatment planning is essential for preventing and effectively managing PFFs. Stem design plays a crucial role in fracture risk, and the use of classification systems supports optimal decision-making. Surgical expertise and multidisciplinary coordination are paramount in managing complex cases.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".