Bisphosphonates can prevent recurrent hip fracture and reduce the mortality in osteoporotic patient with hip fracture: A meta-analysis
Bibliographic record
Abstract
Objective: This meta-analysis was conducted to investigate the efficacy of bisphosphonates for preventing recurrent hip fracture and reducing the mortality of elderly patient with hip fracture.Methods: The databases of Pubmed, Embase and Cochrane Library were searched. All randomized or prospective matched controlled trials that assessed the efficacy of bisphosphonate for elderly patients with hip fracture were included. Two researchers independently extracted data of the included articles and assessed the methodological quality which was assessed based on Jadad scoring system or Newcastle-Ottawa scale. The second hip fracture incidence, mortality and complications were compared between bisphosphonates and control groups.Results: Four studies including 3088 patients were included. Results showed that there were significant difference of second hip fracture (P<0.05) and mortality (P<0.05) between bisphosphonates group and control group. While no significant intergroup difference were observed for all complications.Conclusions: Bisphosphonates can prevent subsequent hip fracture, reduce the mortality, and does not increase the overall complications in elderly patients with hip fracture.doi: http://dx.doi.org/10.12669/pjms.322.9435How to cite this:Peng J, Liu Y, Chen L, Peng K, Xu Z, Zhang D, et al. Bisphosphonates can prevent recurrent hip fracture and reduce the mortality in osteoporotic patient with hip fracture: A meta-analysis. Pak J Med Sci. 2016;32(2):499-504.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".