Outcomes of distal femoral arthroplasty after periprosthetic fractures: minimum 2-year follow-up
Bibliographic record
Abstract
PURPOSE: distal femoral periprosthetic fracture (DFPPF) is a serious complication following total knee arthroplasty (TKA). Recently, treatment of DFPPF with distal femoral arthroplasty (DFA) has gained popularity because of its posited benefits for both patients and the medical system. Short-term follow-up trials investigating DFA have demonstrated acceptable results with regards to function, pain relief and lower postoperative complications than ORIF in elderly patients. The purpose of the current study was to evaluate a consecutive series of DFPPF treated with DFA, with a minimum 2- year follow-up. METHODS: We performed a retrospective study asses- sing the outcomes of distal femoral arthroplasty (DFA) for patients diagnosed with DFPPF. RESULTS: Twenty patients were identified. The mean age of patients was 76.3 (SD, 9.41), the average time from the fracture to revision surgery was 6.7 days (SD, 11.35), The average operative time was 93.5 minutes (SD,16.6). The average follow-up time was 50.15 months (SD, 20.87). During this time, two patients (10%) had complications. One patient experienced a knee dislocation and the second patient had recurrent periprosthetic infections. At final follow up, the mean knee society score was 86.25 (SD, 9.44), the mean Forgotten joint score was 62.16(SD, 23.45) and 93.7 percent of patients were ambulatory. CONCLUSION: DFA following DFPPF is associated with high success rates and provides patients with the opportunity for return of function in a safe and reliable manner.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".