Chronic Liver Disease and the Risk of Osteoporotic Fractures: A Meta-Analysis
Bibliographic record
Abstract
Introduction Chronic liver disease (CLD) causes more than 1 million deaths every year and remains a pandemic in the last decade affecting more than 600,000 patients in the United States. Previous studies found patients with CLD had increased risk of osteoporosis, so fractures were inferred to be complications of this condition. The aim of this meta-analysis is to summarize the best evidence that correlates CLD patients and the risk to develop osteoporotic fractures versus control patients without CLD. Methods A review of the literature using MEDLINE and EMBASE database was performed during December 2017. We included cross-sectional and cohort studies that reported relative risks (RR), odds ratios (OR) and hazard ratios (HR) comparing the risk of developing osteoporotic fractures among patients with CLD versus patients without CLD. Pooled OR and 95% confidence interval (CI) were calculated using generic inverse- variance method. The Newcastle-Ottawa scale was used to determine the quality of the studies. Effect estimates from the individual study were extracted and combined using the random-effect, generic inverse variance method of DerSimonian and Laird. Results After the review of the literature, seven studies fulfilled the eligibility criteria established during the analysis. Significant association was found between CLD and osteoporotic fractures with a pooled OR of 2.13 (95% CI, 1.79 - 2.52). High heterogeneity among the studies was found (I2=88.5). No publication bias was found using Egger regression test (p=0.44). Conclusion We found a significant association between CLD and the risk of developing osteoporotic fractures. The calculated risk was 2.13 times higher for patients with CLD when compared with controls. The results showed high heterogeneity but no publication bias. More prospective studies are needed to fully understand the mechanisms involved in loss of bone density and osteoporotic fractures in order to improve the morbidity associated with this disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".