Effects of encapsulated green tea and Guarana extracts containing a mixture of epigallocatechin-3-gallate and caffeine on 24 h energy expenditure and fat oxidation in men
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been reported that green tea has a thermogenic effect, due to its caffeine content and probably also to the catechin, epigallocatechin-3-gallate (EGCG). The main aim of the present study was to compare the effect of a mixture of green tea and Guarana extracts containing a fixed dose of caffeine and variable doses of EGCG on 24 h energy expenditure and fat oxidation. Fourteen subjects took part to this randomized, placebo-controlled, double-blind, cross-over study. Each subject was tested five times in a metabolic chamber to measure 24 h energy expenditure, substrate oxidation and blood pressure. During each stay, the subjects ingested a capsule of placebo or capsules containing 200 mg caffeine and a variable dose of EGCG (90, 200, 300 or 400 mg) three times daily, 30 min before standardized meals. Twenty-four hour energy expenditure increased significantly by about 750 kJ with all EGCG-caffeine mixtures compared with placebo. No effect of the EGCG-caffeine mixture was observed for lipid oxidation. Systolic and diastolic blood pressure increased by about 7 and 5 mmHg, respectively, with the EGCG-caffeine mixtures compared with placebo. This increase was significant only for 24 h diastolic blood pressure. The main finding of the study was the increase in 24 h energy expenditure with the EGCG-caffeine mixtures. However, this increase was similar with all doses of EGCG in the mixtures.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it