The treatment of periprosthetic femoral fractures after total hip arthroplasty
Bibliographic record
Abstract
Periprosthetic femoral fracture (PFF) is one of severe complications after total hip arthroplasty (THA). As the number of patients receiving THA increased recently, the incidence of PFFs also increased dramatically. There are a number of risk factors for PFFs, such as age, sex, falling and prosthesis loosening. The Vancouver classification system is the most commonly used classification method for PFFs. According to the fracture location, PFFs can be divided into type A intertrochanteric fracture, type B fracture around the stem and type C fracture beyond the stem. The Vancouver type B PFF is further subdivided into type B1 with a well-fixed prosthesis, type B2 with a loose prosthesis but with adequate bone stock, and type B3 with a loose prosthesis and poor proximal bone stock simultaneously. Currently, there are some controversies in treating PFFs, mainly including whether the stem is fixed or not, whether the prosthesis needs to be revised, the selection of the stem, the reconstruction of bone defects, and the methods of fracture fixation. We searched literatures related to PFFs after THA. The incidence, risk factors, classification methods, treatment principles and strategies of PFFs were summarized in the present study. Based on our long-term clinical experience, we evaluated the advantages and disadvantages of each treatment method and provided considerations for the clinical research and selection in treating PFFs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".