Convicting a wrong molecule?
Bibliographic record
Abstract
To the Editor, Dr. Tilg's brilliant insights on metabolic diseases are well-known.However, we do not agree with his commentary on artificial sweeteners appeared in the New England Journal of Medicine [1].The results presented by numerous bench scientists require a focused re-appraisal, because these scientists are not familiar with confounding or biases [2].For example, Dr. Tilg quoted the report by Zani et al. that sucralose inhibited T-cell mobilization in mice.Since sugar is the key energy source for immune-activation [3], this study proves that sucralose is not a major source of sugar.The second article Dr. Tilg quoted was by Suez et al. who fed mice a high fat diet and saccharin and observed glucose intolerance.High fat diet is an independent risk factor for glucose intolerance [4].Thus, we cannot blame saccharin as the true culprit of glucose intolerance when two risk factors coexist.The third article Dr. Tilg cited was Witkowski and colleagues' report.They claimed that erythritol increased platelet activation.However, the process generating platelet-richplasma which Witkowski et al. used can activate platelets [5].Moreover, erythritol can be synthesized endogenously from glucose via the pentose-phosphate-pathway and those who developed obesity have 15-fold higher blood erythritol levels than those without obesity [6].Thus, glucose may be the key contributor to obesity which activates platelets.When we examined the distribution of CVD risk factors per erythritol levels from Witkowski et al.'s supplementary table (our Table 1), highly positive correlations between cardiovascular risk factors and erythritol levels emerged.Thus, it is likely that major cardiac events in the study of Witkowski et al. may be due to the underlying cardiovascular risk factors and erythritol may be an epiphenomenon.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".