What do we really know about the role of caffeine on urinary tract symptoms? A scoping review on caffeine consumption and lower urinary tract symptoms in adults
Bibliographic record
Abstract
AIMS: The purpose of this scoping review was to map out the existing literature on caffeine intake and lower urinary tract symptoms (LUTS) in adults. METHODS: In this scoping review, we searched for all studies available until June 2019 in MEDLINE, Embase, CINAHL, Cochrane Central Register, PsycINFO, LILACS, LiSSa, Web of Science, and Joanna Briggs Institute electronic databases, in addition to a hand search of the bibliographies of all relevant articles and a gray literature search. Both intervention studies on the effects of caffeine reduction in adults with LUTS and observational studies on the association between caffeine intake and LUTS-related outcomes in adults were included and assessed for methodological quality by two independent reviewers. RESULTS: Fourteen intervention and 12 observational studies were included. Overall, there was a decrease in urgency episodes (level of evidence 2, grade of recommendation B) and nocturnal enuresis episodes (4, C) with caffeine reduction. Observational studies reported an unclear association between caffeine intake and LUTS-related outcomes. Most importantly, this present review highlighted high heterogeneity in the studied populations, caffeine measures, and reported outcomes. There was also unknown or high risk of bias in most identified studies. CONCLUSIONS: Caffeine reduction appears to reduce LUTS. Future studies on caffeine reduction interventions should target populations with urgency and urge urinary incontinence, which show the most promising results, and include valid and reliable measures of caffeine intake and LUTS. Finally, future studies should also use reporting guidelines to ensure lower risk of bias.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".