Risk factors for the second contralateral hip fracture in elderly patients: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To achieve a quantitative and comprehensive conclusion concerning the risk factors for the second contralateral hip fracture in elderly patients with initial hip fractures. DATA SOURCES: This search was applied to Medline, Embase, Cochrane central database (all up to April 2014). METHODS: All the studies on bilateral hip fractures in elderly patients published in English were reviewed and qualities of included studies were assessed using the Newcastle-Ottawa Scale. All the data were carefully and independently abstracted by two reviewers, any disagreement was settled by discussion. Data was pooled and a meta-analysis completed. RESULTS: A total of 13 case-control studies were identified for the meta-analysis. The significant risk factors were female (odds ratio (OR), 1.30; 95% confidence interval (CI), 1.02-1.64), living in institutions (OR, 2.53; 95% CI, 1.33-4.85), osteoporosis (OR, 10.02; 95% CI, 5.41-18.57), low vision (OR, 2.09; 95% CI, 1.06-4.12), dementia (OR, 2.02; 95% CI, 1.54-2.65), dizziness (OR, 2.87; 95% CI, 1.42-5.87) cardiac diseases (OR, 1.33; 95% CI, 1.00-1.78) and respiration diseases (OR, 2.58; 95% CI, 1.22-5.47). No significant difference was found in admission age between patients with the unilateral hip fracture and the first hip fracture of bilateral hip groups (standardized mean difference, 0.02, 95% CI, -0.30 to 0.35]. CONCLUSIONS: Patients involved with female, living in institutions, osteoporosis, low vision, dizziness, dementia, respiration diseases and cardiac diseases were at risk for a second contralateral hip fracture after the initial hip fracture.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".