Contemporary management of periprosthetic femur fracture following total hip arthroplasty
Bibliographic record
Abstract
Periprosthetic fractures following total hip arthroplasty (THA) represent a growing clinical challenge due to the increasing prevalence of hip replacements, particularly in an aging population.This review aims to provide a comprehensive analysis of the epidemiology, classification systems, and management strategies for periprosthetic femur fractures, focusing on the unique challenges posed by varying fracture types and patient-specific factors.These fractures significantly impact patient outcomes, often leading to compromised joint function, prolonged recovery, and higher morbidity and mortality.The scope of this manuscript includes a detailed evaluation of the Vancouver classification system, its clinical utility, and emerging insights into patient-specific treatment algorithms.Treatment options range from internal fixation, primarily used for stable prostheses, to revision arthroplasty in cases where the implant is loose or bone quality is poor.Additionally, this paper discusses recent advances in surgical techniques and prosthetic design innovations aimed at improving patient outcomes, with a focus on elderly patients with osteoporosis or multiple comorbidities.Continued innovation in fixation methods and implant technology is essential to improving outcomes for patients with periprosthetic hip fractures.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".