Soy Foods and the Risk of Fracture: A Systematic Review of Prospective Cohort Studies
Bibliographic record
Abstract
OBJECTIVES: The primary objective of our study was to systematically review all available prospective cohort studies which investigated the association of soy food intake and incident fracture risk. METHODS: We searched PubMed, Scopus, and Embase databases for relevant studies up to June 2021. SYNTHESIS: Of 695 records, a total of 5 cohort studies were included in the current systematic review. Two studies that were performed in China evaluated hip fracture while 2 studies that were done in Singapore evaluated any kind of fractures. The other study was conducted in Japan and evaluated osteoporosis fractures. All studies used a face-to-face interview to assess the dietary intake of soy foods. All 5 cohort studies were determined to be of high quality. One study considered soy food as a part of a vegetables-fruit-soy food dietary pattern. Others reported the association of dietary intake of soy foods with the risk of fractures. CONCLUSION: The evidence from prospective cohort studies was suggestive for a protective role of soy foods, alone or within a dietary pattern, in the risk of incident fracture among Asian women, particularly for those in early menopause and those who used fermented soy products. But for men, the association was not significant. However, more cohort studies, including non-Asian populations, are required to confirm this association fully.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".