Risk assessment of nutrients: There must be a threshold for their effects
Bibliographic record
Abstract
Nutrients serve physiological functions in a dose-dependent manner and that needs to be recognized in risk assessment. An example of the consequences of not properly considering this can be seen in a recent assessment by the European Food Safety Authority (EFSA). EFSA concluded in 2022 that the intake of added and free sugars should be "as low as possible in the context of a nutritionally adequate diet". That conclusion of EFSA is based on the effects on two surrogate endpoints for an adverse effect found in randomized controlled trials with high sugars intake levels: fasting glucose and fasting triglycerides. The lowest intake levels in these trials were around 10 energy% and at this intake level there were no adverse effects on the two outcomes. This indicates that the adverse effects of sugars have an observable threshold value for these two endpoints. The most appropriate interpretation from the vast amount of data is that currently no definitive conclusion can be drawn on the tolerable upper intake level for dietary sugars. Therefore, EFSA's own guidance would lead to the conclusion that the available data do not allow the setting of an upper limit for added sugars and hence, that more robust data are required to identify the threshold value for intake of sugars.
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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 | 0.040 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".