Total Femoral Replacement for Salvage of Periprosthetic Fractures
Bibliographic record
Abstract
Patients with complex periprosthetic fracture patterns of the femur may ultimately require amputation. Some authors have described the use of mega-endoprostheses as a salvage procedure. This study reports functional outcome, complications, and implant and patient survival after total femoral replacement (TFR) for salvage of periprosthetic fracture of the femur. A prospective database of 20 consecutive patients who underwent TFR for salvage of a periprosthetic fracture was compiled. Patient demographics, mobility information, and preoperative and postoperative Short Form 12-item Survey (SF-12) and 1-year Toronto Extremity Salvage Score (TESS) data were recorded. Postoperative complications were obtained from the hospital database and patient medical notes. One patient was lost to follow-up and was excluded from analysis. The study included 8 men and 11 women, with a mean age of 68.4 years. No significant difference was noted in the prefracture physical (4.4; P=.13) or mental (0.3; P=.78) component scores of the SF-12 compared with 1-year scores. The TESS at 1 year was 69%. However, patients were more likely to require a walking aid postoperatively (P<.0001). One-fourth of the patients had a postoperative medical complication. In addition, 1 patient had a dislocation and 2 patients had a periprosthetic infection. The implant survival rate was 86% at 10 years; however, the 10-year mortality rate was 58%. Although TFR for salvage of a periprosthetic fracture of the femur offers good functional outcome and implant survival, it is at the expense of postoperative complications, and TFR is associated with a high long-term mortality rate.
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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 | 0.001 |
| 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) | 0.003 | 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".