Hemoglobin adducts of acrylamide in human blood – What has been done and what is next?
Bibliographic record
Abstract
Acrylamide forms in many commonly consumed foods. In animals, acrylamide causes tumors, neurotoxicity, developmental and reproductive effects. Acrylamide crosses the placenta and has been associated with restriction of intrauterine growth and certain cancers. The impact on human health is poorly understood and it is impossible to say what level of dietary exposure to acrylamide can be deemed safe as the assessment of exposure is uncertain. The determination of hemoglobin (Hb) adducts from acrylamide is increasingly being used to improve the exposure assessment of acrylamide. We aim to outline the literature on Hb adduct levels from acrylamide in humans and discuss methodological issues and research gaps. A total of 86 studies of 27,966 individuals from 19 countries were reviewed. Adduct levels were highest in occupationally exposed individuals and smokers. Levels ranged widely from 3 to 210 pmol/g Hb in non-smokers from the general population and this wide range suggests that dietary exposure to acrylamide varies largely. Non-smokers from the US and Canada had slightly higher levels as compared with non-smokers from elsewhere, but differences within studies were larger than between studies. Large studies with exposure assessment of acrylamide and related adduct forming compounds from diet during early-life are encouraged for the evaluation of health effects.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".