Outcomes following surgical treatment of periprosthetic femur fractures: a single centre series
Bibliographic record
Abstract
BACKGROUND: Periprosthetic femoral fracture after total hip arthroplasty (THA) is an increasing clinical problem and a challenging complication to treat surgically. The aim of this retrospective study was to review the treatment of periprosthetic fractures and the complication rate associated with treatment at our institution. METHODS: We reviewed the cases of patients with periprosthetic femoral fractures treated between January 2004 and June 2009. We used the Vancouver classification to assess fracture types, and we identified the surgical interventions used for these fracture types and the associated complications. RESULTS: We treated 45 patients with periprosthetic femoral fractures during the study period (15 men, 30 women, mean age 78 yr). Based on Vancouver classification, 2 patients had AL fractures, 9 had AG, 15 had B1, 24 had B2, 2 had B3 and 4 had C fractures. Overall, 82% of fractures united with a mean time to union of 15 (range 2-64) months. Fourteen patients (31%) had complications; 11 of them had a reoperation: 6 to treat an infection, 6 for nonunion and 2 for aseptic femoral component loosening. CONCLUSION: Periprosthetic fractures are difficult to manage. Careful preoperative planning and appropriate intraoperative management in the hands of experienced surgeons may increase the chances of successful treatment. However, patients should be counselled on the high risk of complications when presenting with this problem.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".