Effects of Tea Consumption on Risk of Osteoporotic Bone Fracture in Older People: Meta-Analysis of Observational studies
Bibliographic record
Abstract
Objectives: There have been several studies published in the medical literature over the past 30 years that address the association between tea consumption and osteoporosis with inconsistent findings. A meta-analysis was undertaken, including 8 studies, to determine the effect of tea consumption on the risk of fracture. Methods & Materials: This systematic review and meta-analysis conducted on articles published from 1980 to 2010. We searched the following electronic databases: medline, pubmed, ISI, Embase and Chocrane and also reverent journals using mesh search terms including caffeine, tea, coffee, osteoporosis, Bone Mineral Density (BMD) and fracture*. All the relevant English written articles reviewed by two independent researchers. After title and abstract review non-relevant articles were excluded. The full text of accepted publications was obtained and their content reviewed for final inclusion. Using MOOSE (Meta-analysis Of Observational Studies in Epidemiology) criteria, relevant articles with high quality, reporting odds ratio (OR) or risk ratio (RR) for fracture following tea consumption, selected for meta-analysis. Results: Four hundred and twenty one articles found through the primary searches 78 full text articles evaluated. Only 8 of them fulfilled all the inclusion criteria and their relevant data were extracted included into the analysis. The meta-analysis showed that tea consumption can have a protective effect on the risk of hip fracture which is not significant (RR=0.872, 0.733-1.038). Analysis by type of the studies suggests that according to cohort studies there is a significant decrease in the risk of hip fracture following tea consumption (RR=0.749, 0.603-0.929) while case-control studies do not support this findings (RR=1.157, 0.863-1.553). Conclusion: Tea as a popular drink throughout the world can reduce the risk of osteoporotic bone fractures along with its known antioxidant effects.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.057 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".