Relationship between caffeine intake and infertility: a systematic review of controlled clinical studies
Bibliographic record
Abstract
BACKGROUND: For a long time, the relationship between caffeine consumption and infertility in the general population is unclear, this study is aimed to systematically review the evidence from any type of controlled clinical studies to explore whether caffeine intake is a risk factor for human infertility. METHODS: Seven databases were searched from inception to May 2019. We included women/men without a history of infertility but were willing to have children in prospective studies and women/men who were diagnosed with infertility in retrospective studies. The observed exposure factor should be caffeine or caffeine containing beverage. Diagnosis of infertility or not for participants was the key outcome. The Newcastle-Ottawa scale (NOS) or Cochrane risk of bias tool were used to assess the methodological quality of included studies. Meta-analysis was conducted if there were acceptable clinical and statistical heterogeneity among studies. The GRADE method was used to assess the certainty of the evidence. RESULTS: Four studies (one cohort study and three case-control studies) involving 12,912 participants were included. According NOS, the average score of case-control studies was 6, and the cohort study achieved 9. Meta-analysis and subgroup analysis were conducted. The results showed that low (OR 0.95, 95%CI 0.78-1.16), medium (OR 1.14, 95%CI 0.69-1.86) and high doses (OR 1.86, 95%CI 0.28-12.22) of caffeine intake may not increase the risk of infertility. The quality of the current evidence bodies were all low. CONCLUSION: Our study provides low quality evidence that regardless of low, medium and high doses of caffeine intake do not appear increase the risk of infertility. But the conclusion should be treated with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".