Folic Acid Fortification and Supplementation-Good for Some but Not So Good for Others
Bibliographic record
Abstract
Evidence has established the protective effect of folic acid (FA) fortification and periconceptional supplementation on neural tube defects (NTDs). Folic acid fortification and periconceptional supplementation of women may reduce the risk of certain childhood cancers in their offspring. However, recent human studies have suggested that FA supplementation and fortification may promote the progression of already existing, undiagnosed, preneoplastic and neoplastic lesions, thereby corroborating earlier observations from animal and in vitro studies. Following the success of mandatory FA fortification on the reduction of NTD rates in the United States and Canada, several countries are currently considering whether or not, and at what dose, to institute FA fortification. Future debates and decisions regarding FA fortification should take into consideration all potential adverse effects and dose-responses of such a measure because it may be associated with very serious consequences for many generations. In addition to careful monitoring of adverse effects, preclinical and population-based studied are warranted in order to determine the efficacy, safety, and potential deleterious effects of FA fortification and supplementation on cancer risk and other health outcomes.
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.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".