Risk factors for the periprosthetic fracture after total hip arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: A systematic review and meta-analysis was performed to investigate the risk factors associated with periprosthetic fracture after total hip arthroplasty. MATERIAL AND METHODS: We searched potential studies in the following databases: MEDLINE, Embase, Web of Science, SCOPUS and Cochrane CENTRAL up to December 2013. Newcastle-Ottawa Scale was used to evaluate the methodological quality, and Stata 11.0 was used to perform all the analyses. RESULTS: Seven studies altogether, including 1069 cases of periprosthetic fractures and 74,776 controls, were included in the meta-analysis. Compared to those absent following demographic or medical conditions, patients involved with female gender (odds ratio, 1.534; p < 0.001), advanced age (>80) (odds ratio: 4.203; p < 0.001), revision (odds ratio: 4.398; p < 0.001), rheumatoid arthritis (odds ratio: 2.503; p < 0.001), osteonecrosis (odds ratio: 1.563; p = 0.009), and implant type of Exeter (odds ratio: 1.511; p = 0.017) were more likely to sustain periprosthetic fractures. Osteoarthritis (vs not) (odds ratio: 0.449; p < 0.001) was identified a protective factor for periprosthetic fractures after total hip arthroplasty. The other factors, including lower ages, American Society of Anesthesiologists ≥ 3, and other implant types, were not significant risk factors for periprosthetic fractures. CONCLUSIONS: These medical conditions as reminder should be kept in clinicians' mind and close follow-up should be implemented in patients involved for preventing the occurrence of periprosthetic fractures after total hip arthroplasty.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".