Dietary patterns and semen quality: a systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
Summary A number of studies have examined the association between dietary patterns and semen quality, but the findings have been inconclusive. Herein, we conducted a systematic review and meta‐analysis of observational studies to assess the association between dietary patterns and semen quality. PubMed, Cochrane library, Science direct, Scopus, Google Scholar, and ISI web of science databases were searched up to August 2016 for observational studies assessing the association between common dietary patterns and sperm quality markers. Data were pooled by the generic inverse variance method with random effects and expressed as mean differences with 95% confidence intervals (CIs). Heterogeneity was assessed (Cochrane Q‐statistic) and quantified (I2‐statistic). The Newcastle‐Ottawa Scale assessed study quality. Six eligible studies, involving 8207 participants, were included in our systematic review and meta‐analysis. The pooled mean difference of sperm concentration for the healthy dietary pattern versus unhealthy dietary pattern intake was mean difference: 0.66; 95% CI, 0.305–1.016; p < 0.001. In comparison with those who had the highest adherence to healthy dietary pattern, individuals in the lowest adherence had significantly lower level of sperm concentration. However, no significant association was seen between eating patterns and other semen quality. Healthy dietary pattern seems to be associated with elevated sperm concentration level. Further longitudinal studies are needed to clarify this relationship.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".