Management of periprosthetic femoral fractures following total hip replacement; A Case Series
Bibliographic record
Abstract
Background: Periprosthetic femoral fractures following total hip arthroplasty (THA) are not very uncommon. At present the Vancouver classification provides management algorithm for deciding treatment options but treatment options may vary between surgeons, where as in this study most patients managed were according to Vancouver classification management algorithm. The most common treatment modality for treating periprosthetic femoral fractures around a well-fixed stem is with osteosynthesis, but fracture with loose stem requires revision arthroplasty and fracture with poor bone requires bone graft augmentation. Methods: We reviewed 21 consecutive cases with periprosthetic femoral fractures in association with THA between June 2018 and December 2020. Locking and non locking compression plates, wires, cables system were used for osteosynthesis. Most of fractures were managed according to Vancouver classification management algorithm but modified in some cases according to the surgeon’s skills and judgment. Results: According to Vancouver classification, two patients had AL fractures, two patients had AG fractures, twelve Patients had B1, five patients had B2, two patients had B3 and one patient had type C fracture. Of these two cases were treated by conservatively, sixteen cases were treated by osteosynthesis, three cases by revision arthroplasty. Conclusion: The careful analysis of implant stability and fracture patterns is crucial for the optimal treatment of Periprosthetic femoral fractures. Expert Surgeon’s skills are needed to deal with periprosthetic femoral fractures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".