Assessing the in vivo data on low/no-calorie sweeteners and the gut microbiota
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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