Is caffeine intake a risk factor leading to infertility? A protocol of an epidemiological systematic review of controlled clinical studies
Bibliographic record
Abstract
BACKGROUND: Previous studies showed that high dose of caffeine intake may induce some specific human reproductive system diseases, even lead to infertility. OBJECTIVES: In consideration of the high consumption of caffeine according to the latest population-based survey, this review is aimed to systematically review the evidence from all controlled clinical studies of caffeine intake for infertility. DESIGNS: Relevant randomized/quasi-randomized controlled trials, non-randomized clinical studies, cohort studies, and case-control studies will be included in this review. Participants will be either those without a history of infertility who are willing to have a baby (for prospective studies) or infertile patients with confirmed diagnosis (for retrospective studies). Caffeine or caffeine-containing beverage will be observed as the exposure factor. The key outcome will be the diagnosis of infertility in participants. All relevant published/unpublished or ongoing studies will be searched from seven databases and four online systems until December 2015. Two authors will screen the literatures and extract the data independently. Methodological quality of the included studies will be assessed by two authors according to either Risk of Bias Assessment or Newcastle-Ottawa Scale. We will use R software to analyze the data. Dose of caffeine will be quantified on a daily basis, and relative risk with their 95 % confidence interval will be measured. If data permit, meta-analysis and dose-response analysis will be conducted. Summary of findings tables will be generated using Guideline Development Tool online. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015015714.
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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 | 0.160 | 0.178 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".