Optimal surgical treatment for periprosthetic distal femoral fractures after total knee arthroplasty: a Bayesian-based network analysis
Bibliographic record
Abstract
BACKGROUND: The surgical methods for periprosthetic distal femoral fractures (PDFFs) after total knee arthroplasty included locking compression plate (LCP), retrograde intramedullary nailing (RIMN), and distal femoral replacement (DFR). However, the optimal treatment remains controversial. We performed a network meta-analysis (NMA) to provide the optimal surgical method for PDFFs. MATERIALS AND METHODS: Electronic databases, including Embase, Web of Science, Cochrane Library, and PubMed, were searched for studies that compared LCP, RIMN, and DFR for PDFFs. The quality of the included studies was assessed according to the Newcastle-Ottawa scale. Pairwise meta-analysis was performed by Review Manager version 5.4. The NMA was conducted in Aggregate Data Drug Information System software version 1.16.5. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for postoperative complications and reoperations. RESULTS: A total of 19 studies and 1198 patients were included, of whom 733 for LCP, 282 for RIMN, and 183 for DFR. Pairwise meta-analysis comparing LCP to RIMN and LCP to DFR showed no significant difference in complications and reoperations except that RIMN had a higher risk of malunion comparing to LCP (OR 3.05; 95% CI 1.46-6.34; P = 0.003). No statistically significant effects were found in the NMA of overall complications, infection, and reoperation. However, results of rank probabilities showed that DFR ranked best in overall complications and reoperation, RIMN ranked best in infection but worst in reoperation, and LCP ranked worst in infection and middle in reoperation. DISCUSSION: We found similar complication rate and reoperation rate between LCP, RIMN, and DFR. The results of rank probabilities favored DFR, and further studies with high-level evidence are expected to verify the optimal surgical method for PDFFs. LEVEL OF EVIDENCE: Level II; network meta-analysis.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".