Risk Factors for Periprosthetic Fractures After Total Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Periprosthetic fractures (PPFs) following total knee arthroplasty (TKA) result in repeated hospitalizations and incur substantial medical costs. However, the risk factors contributing to PPFs post-TKA remain highly debated. This study provides a comprehensive and quantitative analysis of these risk factors, offering scientific evidence to guide the development of clinical prevention strategies. METHODS: A systematic literature search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library databases up to October 1, 2024. We included all English-language studies that evaluated the risk factors for PPFs after TKA. The Newcastle-Ottawa Scale was leveraged to appraise the quality of the included studies, while odds ratios (OR) and 95% confidence intervals (CI) were used to assess the associations between various risk factors and the likelihood of PPFs after TKA. This protocol was registered with the International Prospective Register of Systematic Reviews (registration number: CRD42024601636). A total of 24 studies were included, encompassing 3,759,394 TKA cases. RESULTS: The meta-analysis identified several risk factors for PPFs after TKA, including sex (OR = 1.81, 95% CI: 1.12 to 2.92), presence of an anterior femoral notching (OR = 3.12, 95% CI: 1.35 to 7.20), osteoporosis (OR = 1.68, 95% CI: 1.51 to 1.87), Parkinson's disease (OR = 7.48, 95% CI: 1.15 to 48.83), cardiovascular diseases (OR = 2.23, 95% CI: 1.01 to 4.94), and laterality (OR = 1.68, 95% CI: 1.01 to 2.79). CONCLUSIONS: Sex, osteoporosis, presence of anterior femoral notching, laterality, Parkinson's disease, and cardiovascular diseases are significant risk factors for PPFs after TKA.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".