Surgical Fixation of Vancouver Type B1 Periprosthetic Femur Fractures
Bibliographic record
Abstract
OBJECTIVES: Vancouver type B1 periprosthetic femur fractures occur around a stable implant and are typically treated with open reduction and internal fixation (ORIF). Different fixation techniques are described in the literature, and there is a lack of consensus regarding the best operative fixation strategy. The purpose of this investigation was to systematically review and compare the most commonly used fixation strategies for these fractures. DATA SOURCES: A database search was performed using PubMed, MEDLINE, and Cochrane databases to identify studies published in English language from 1985 to 2013. STUDY SELECTION: Articles with a minimum of 5 patients with type B1 periprosthetic femur fractures and containing outcome data regarding nonunion, malunion, infection, and reoperation rate were included. DATA EXTRACTION: Studies were analyzed and categorized into 4 groups: group 1: ORIF with cortical strut allografts alone, group 2: ORIF with cable plate/compression plates alone, group 3: ORIF with cable plate/compression plates and cortical strut allograft, group 4: ORIF with locking plates alone. Individual patient outcomes were extracted for each study and pooled for each of the 4 groups. Data analysis was performed comparing rates of nonunion, malunion, hardware failure, infection, and reoperation. DATA SYNTHESIS: Data were analyzed using Review Manager and SAS 9.3. CONCLUSIONS: In total, 333 patients identified with an overall rate of 5% nonunion, 6% malunion, 5% infection, 4% hardware failure, 9% reoperation, and 15% total complications. When comparing outcomes for different modes of fixation, compared with cable plate/compression plate systems, locking plates had a significantly higher rate of nonunion (3% vs. 9% P = 0.02) and a trend toward a higher rate of hardware failure (2% vs. 7%, P = 0.07). There are limitations to this study, and further investigation with high-quality randomized controlled trials is needed to effectively compare treatment strategies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| 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".