Assessing the in vivo data on low/no-calorie sweeteners and the gut microbiota
Bibliographic record
Abstract
Low/no-calorie sweeteners (LNCS) are continually under the spotlight in terms of their safety and benefits; in 2014 a study was published linking LNCS to an enhanced risk of glucose intolerance through modulation of the gut microbiota. In response, an in-depth review of the literature was undertaken to evaluate the major contributors to potential changes in the gut microbiota and their corresponding sequelae, and to determine if consuming LNCS (e.g., acesulfame K, aspartame, cyclamate, neotame, saccharin, sucralose, steviol glycosides) contributes to changes in the microbiome based on the data reported in human and animal studies. A few rodent studies with saccharin have reported changes in the gut microbiome, but primarily at high doses that bear no relevance to human consumption. This and other studies suggesting an effect of LNCS on the gut microbiota were found to show no evidence of an actual adverse effect on human health. The sum of the data provides clear evidence that changes in the diet unrelated to LNCS consumption are likely the major determinants of change in gut microbiota numbers and phyla, confirming the viewpoint supported by all the major international food safety and health regulatory authorities that LNCS are safe at currently approved levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".