Treatment Algorithm of Periprosthetic Femoral Fracturens
Bibliographic record
Abstract
Introduction. The ever-expanding indications for total hip arthroplasty are leading to more implants being placed in younger as well as in older patients with high functional demand. Also, prolonged life expectancy is contributing to an overall increment of periprosthetic femoral fractures. The Vancouver classification has been the most used for guiding the surgeon choice since its proposal in 1995. Fractures occurring over a hip femoral implant can be divided into intra-operative and post-operative PFFs, and their treatment depends on factors that may severely affect the outcome: level of fracture, implant stability, quality of bone stock, patients’ functional demand, age and comorbidities, and surgeon expertise. There are many different treatment techniques available which include osteosynthesis and revision surgery or a combination of both. The goals of surgical treatment are patients’ early mobilization, restoration of anatomical alignment and length with a stable prosthesis and maintenance of bone stock. Significance. The aim of this review is to describe the state-of-the-art treatment and outcomes in the management of PFFs. We performed a systematic literature review of studies reporting on the management of PFFs around hip stems and inter-prosthetic fractures identifying 45 manuscripts eligible for the analysis. Conclusions. PFFs present peculiar characteristic that must be considered and special features that must be addressed. Their management is complex due to the extreme variability of stem designs, the possibility of having cemented or uncemented stems, the difficulty in identifying the “real” level of the fracture and the actual stability of the stem. As a result, the definition of a standardized treatment is unlikely, thereby high expertise is fundamental for the surgical management of PPFs, so this kind of fractures should be treated only in specialized centres with both high volume of revision joint arthroplasty and trauma surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".