Treatment of Periprosthetic Femoral Fractures in Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: We analyzed the radiological and clinical results of our study subjects according to the management algorithm of the Vancouver classification system for the treatment of periprosthetic femoral fractures in hip arthroplasty. METHODS: We retrospectively reviewed 18 hips with postoperative periprosthetic femoral fractures. The average follow-up was 49 months. The fracture type was determined based on the Vancouver classification system. The management algorithm of the Vancouver classification system was generally applied, but it was modified in some cases according to the surgeon's decision. At the final follow-up, we assessed the radiological results using Beals and Tower's criteria. The functional results were also evaluated by calculating the Harris hip scores. RESULTS: Seventeen of 18 cases (94.4%) achieved primary union at an average of 25.5 weeks. The mean Harris hip score was 92. There was 1 case of nonunion, which was a type C fracture after cemented total hip arthroplasty, and this required a strut allograft. Subsidence was noted in 1 case, but the fracture was united despite the subsidence. There was no other complication. CONCLUSIONS: Although we somewhat veered out of the management algorithm of the Vancouver classification system, the customized treatment, with considering the stability of the femoral stem and the configuration of the fracture, showed favorable overall results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".