100% Fruit Juice Intake, Glucose Profile, and Uric Acid: A Systematic Review
Bibliographic record
Abstract
Several studies suggest a favorable effect of 100% fruit juice (100%FJ) on cardio-metabolic risk. However, international dietary guidelines disagree on recommendations regarding fruit drink consumption, and the published European Food Safety Authority draft, based only on epidemiological data, suggested a direct relationship between 100%FJ intake and the development of type 2 diabetes mellitus (T2DM) and gout. Thus, we performed a systematic review of both randomized controlled trials (RCT) and prospective studies that assessed the relationship between 100%FJ intake, uric acid metabolism, and glucose profile. Concerning previous meta-analyses, no new prospective studies were identified for the evaluation of T2DM, and no studies were identified for the assessment of gout risk in the general population; conversely, 3 studies were detected for the assessment of gestational diabetes risk. Moreover, new RCTs were detected and included in an updated meta-analysis on glucose profile and uric acid. 100%FJ intake was associated with a decrease in uric acid, and neutral associations were found for different markers of glucose metabolism and the risk of gestational diabetes. In summary, this meta-analysis confirmed that there was no difference between 100%FJ intake and the comparator on glucose metabolism. It also showed that 100%FJ intake resulted in lower levels of uric acid than the comparator. In addition, the analysis indicated a neutral effect of 100%FJ on gestational diabetes. Finally, the systematic review confirmed the neutral effect of 100%FJ on T2DM and suggested the need for further studies to evaluate the risk of gout.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".