Selenium and bone health: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Bone health affects the ability of human body to stay active, and its degradation can cause considerable morbidity and mortality. The factors related to bone health play an important role in preventing osteoporosis and its adverse consequences. However, the risk factors for osteoporosis have not been fully elucidated. Deficiency in the trace element selenium may be one of the risk factors for the development of osteoporosis. Previous studies have investigated the effects of selenium on osteoporosis; however, the results are inconclusive. Therefore, the present study aimed to systematically examine the existing literature on the associations between dietary or serum selenium and bone mineral density (BMD), osteoporosis or osteoporotic fractures, and to quantify such associations through meta-analysis. METHODS AND ANALYSIS: PubMed, Embase and Cochrane Library will be searched using a specified search strategy to identify relevant studies up to October 2019. Both interventional and observational studies in humans will be included. The outcomes will include BMD and the prevalence or incidence of osteoporosis and osteoporotic fractures. For dietary or serum selenium and BMD, osteoporosis or osteoporotic fractures pooled analyses, estimates will be expressed as the mean difference, and the pooled OR, relative risk, HR or beta coefficient, and corresponding 95% CIs. Heterogeneity of the studies and publication bias will be investigated accordingly. To assess the quality and the risk of bias of the included studies, the Newcastle-Ottawa Quality Scale or the Cochrane risk of bias assessment tool will be used where appropriate. ETHICS AND DISSEMINATION: Since no private and confidential patient data will be included in the reporting, approval from an ethics committee is not required. The results will be published in a peer-reviewed journal. The study raises no ethical issues. PROSPERO REGISTRATION NUMBER: CRD42019147188.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".