Treatment of acute distal femur fractures above a total knee arthroplasty: Systematic review of 415 cases (1981–2006)
Bibliographic record
Abstract
BACKGROUND: There is no consensus on the best treatment for periprosthetic supracondylar fracture. MATERIAL AND METHODS: We systematically summarized and compared results of different fixation techniques in the management of acute distal femur fractures above a total knee arthroplasty (TKA). Several databases were searched (Medline, Cochrane library, OTA and AAOS abstract databases) and baseline and outcome parameters were abstracted. RESULTS: We extracted data from 29 case series with a total of 415 fractures. The following outcomes were noted: a nonunion rate of 9%, a fixation failure rate of 4%, an infection rate of 3%, and a revision surgery rate of 13%. Retrograde nailing was associated with relative risk reduction (RRR) of 87% (p = 0.01) for developing a nonunion and 70% (p = 0.03) for requiring revision surgery compared to traditional (non-locking) plating methods. Point estimates also suggested risk reductions for locking plates, although these were not statistically significant (57% for nonunion, p = 0.2; 43% for revision surgery, p = 0.23) compared to traditional plating. RRRs for nonunion and revision surgery were also statistically significantly lower for retrograde nailing and locking plates compared to nonoperative treatment. INTERPRETATION: Modern-day treatment methods are superior to conventional treatment options in the management of distal femur fractures above TKAs. The results should be interpreted with caution, due to the lack of randomized controlled trials and the possible selection bias in case series.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".