Risk factors for dislocation after revision total hip arthroplasty: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: No formal systematic review or meta-analysis was performed up to now to summarize the risk factors of dislocation after revision total hip arthroplasty(THA). AIMS: The present study aimed to quantitatively and comprehensively conclude the risk factors of dislocation after revision total hip arthroplasty. METHODS: A search was applied to CNKI, Embase, Medline, and Cochrane central database (all up to October 2016). All studies assessing the risk factors of dislocation after revision THA without language restriction were reviewed, and qualities of included studies were assessed using the Newcastle-Ottawa Scale. Data were pooled and a meta-analysis completed. RESULTS: A total of 8 studies were selected, which altogether included 4656 revision THAs. 421 of them were cases of dislocation occurred after surgery, suggesting the accumulated incidence of 9.04%. Results of meta-analyses showed that age at surgery (standardized mean difference -0.222; 95% CI -0.413-0.031), small-diameter femoral heads (≤28 mm) (OR 1.451; 95%CI 1.056-1.994), history of instability (OR 2.739; 95%CI 1.888-3.974), number of prior revisions ≥ 3 (OR, 2.226; 95% CI, 1.569-3.16) and number of prior revisions ≥ 2 (OR 1.949; 95% CI 1.349-2.817), acetabular components with elevated rim liner were less likely to develop dislocation after revision THA (OR 0.611; 95% CI 0.415-0.898). CONCLUSIONS: Related prophylaxis strategies should be implemented in patients involved with above-mentioned risk factors to prevent dislocation after revision THA.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| 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".