Relation of fruit juice with adiposity and diabetes depends on how fruit juice is defined: a re-analysis of the EFSA draft scientific opinion on the tolerable upper intake level for dietary sugars
Bibliographic record
Abstract
Low to moderate doses of 100% fruit juice have shown protective associations with cardiovascular disease [ 1 ], stroke [ 2 , 3 ], stroke mortality [ 3 ], metabolic syndrome [ 4 ] and hypertension [ 5 ] in prospective cohort studies. The nutritional value of 100% fruit juice is comparable to whole fruits [ 6 ] and its consumption has been recognized as an option to meet recommendations for fruit and vegetable intake in several nutrition guidelines [ 7 , 8 ]. However, the definition for 100% fruit juice is often unclear in dietary assessment questionnaires which may group together both non-defined sources of fruit juice and 100% fruit juice. These assessments may not capture the true association with 100% fruit juice, as non-defined sources of fruit juice may include fruit drinks with very little fruit juice and added sugars resembling more sugar sweetened beverages, which have shown the opposite associations with cardiovascular disease [ 9 ], type 2 diabetes [ 10 ], metabolic syndrome [ 11 ], and hypertension [ 12 , 13 ] in many of the same prospective cohort studies.
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.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".