Green tea effects on cognition, mood and human brain function: A systematic review
Bibliographic record
Abstract
BACKGROUND: Green tea (Camellia sinensis) is a beverage consumed for thousands of years. Numerous claims about the benefits of its consumption were stated and investigated. As green tea is experiencing a surge in popularity in Western culture and as millions of people all over the world drink it every day, it is relevant to understand its effects on the human brain. PURPOSE: To assess the current state of knowledge in the literature regarding the effects of green tea or green tea extracts, l-theanine and epigallocatechin gallate both components of green tea-on general neuropsychology, on the sub-category cognition and on brain functions in humans. METHODS: We systematically searched on PubMed database and selected studies by predefined eligibility criteria. We then assessed their quality and extracted data. We structured our effort according to the PRISMA statement. OUTCOME: We reviewed and assessed 21 studies, 4 of which were randomised controlled trials, 12 cross-over studies (both assessed with an adapted version of the DELPHI-list), 4 were cross-sectional studies and one was a cohort study (both assessed with an adapted version of the Newcastle-Ottawa assessment scale). The average study quality as appraised by means of the DELPHI-list was good (8.06/9); the studies evaluated with the Newcastle-Ottawa-scale were also good (6.7/9). CONCLUSIONS: The reviewed studies presented evidence that green tea influences psychopathological symptoms (e.g. reduction of anxiety), cognition (e.g. benefits in memory and attention) and brain function (e.g. activation of working memory seen in functional MRI). The effects of green tea cannot be attributed to a single constituent of the beverage. This is exemplified in the finding that beneficial green tea effects on cognition are observed under the combined influence of both caffeine and l-theanine, whereas separate administration of either substance was found to have a lesser impact.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".