A systematic review and meta-analysis of fall incidence and risk factors in elderly patients after total joint arthroplasty
Bibliographic record
Abstract
BACKGROUND: Falls in the elderly have become a serious social problem worldwide. Approximately a third of persons fall at least once in the year after total joint arthroplasty (TJA), but preventing and treating falls is still challenging in clinical practice. Until now, no formal systematic review or meta-analysis was performed to summarize the risk factors of falls after TJA. The present study aimed to quantitatively and comprehensively conclude the risk factors of falls after TJA in elderly patients. METHODS: The electronic databases to be searched include CNKI, Embase, Medline, and Cochrane central database (all up to November 2018). All studies on the risk factors of falls after TJA in elderly patients without language restriction were reviewed. Process of evaluation of identified studies and extraction of data were independently conducted by 2 reviewers, qualities of included studies were assessed using the Newcastle-Ottawa Scale. Data were pooled and a meta-analysis completed. All analyses were performed by the software Stata 11.0. RESULTS: A total of 14 studies were included, which altogether included 1284456 patients with TJA, of them 12879 cases of falls occurred after surgery, suggesting the accumulated incidence of 13.1% and the prevalence of in-hospital falls was 1.0%. This study has provided evidence for the preventing of falls in the elderly patients who were underwent TJA. Outcome measures include advanced age, female, Overweight (BMI≥25 kg/m), falls history, use of walking aid, diabetes, cardiac disease, hypertension, COPD and depressive symptoms. The ABC Scale was significantly negatively correlated with falls after lower extremity joint replacement. CONCLUSIONS: Related prophylaxis strategies should be implemented in elderly patients involved with above-mentioned risk factors to prevent falls after TJA.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.011 | 0.011 |
| 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".