Open Reduction and Internal Fixation for Vancouver B1 and B2 Periprosthetic Femoral Fractures: A Proportional Meta-Analysis
Bibliographic record
Abstract
Purpose: Periprosthetic femoral fracture (PFF) is a common complication after total hip arthroplasty, and open reduction and internal fixation (ORIF) is a common surgical treatment.We conducted a meta-analysis to compare the outcomes of ORIF in patients with different fracture patterns (Vancouver B1 and B2). Materials and Methods:We conducted a systematic search of PubMed, Embase, Cochrane Library and KoreaMed from inception to August 2022.We conducted a pair-wise meta-analysis (with a fixed-effects model) on the 10 comparative studies and a proportional meta-analysis on the data from the 39 articles to determine a consensus.The outcomes were the incidence of reoperations that included osteosynthesis, irrigation/debridement and revision arthroplasty.Results: The pair-wise meta-analysis showed similar outcomes between two groups; the risk of reoperation (odds ratio [OR]=0.82,confidence interval [CI] 0.43-1.55,P=0.542), nonunion (OR=0.49;CI 0.22-1.10,P=0.085) and deep infection (OR=1.89,CI 0.48-7.46,P=0.361).In proportion meta-analysis, pooled prevalence of reoperation was 9% (95% CI, 6-12) in B1 and 8% (95% CI, 2-15) in B2 (heterogeneity between two groups (Q), P=0.772).The pooled prevalence of nonunion was same as of 4% in B1 and B2 (Q, P=0.678), and deep infection was 2% (95% CI, 1-3) in B1 and 4% (95% CI, 2-7) in B2 (Q, P=0.130).Conclusion: ORIF is a feasible treatment for B1 and B2 periprosthetic femoral fractures, with acceptable outcomes in terms of, nonunion and infection.The results of this study would help clinicians and provide baseline data for further studies validating PFF.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.060 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".