Folic acid fortification prevents neural tube defects and may also reduce cancer risks
Bibliographic record
Abstract
UNLABELLED: The prevalence of neural tube defect (NTD)-affected pregnancies ranges between 0.4 and 2/1000 pregnancies in EU. NTDs result in severe malformations and sometimes miscarriages. Children born with NTD suffer for the rest of their life of disability and chronic healthcare issues, and many women therefore choose termination of pregnancy if NTD is diagnosed prenatally. Women planning for pregnancy are recommended to eat 400 μg folic acid/d, whereas average figures across Europe indicate intakes of ∼250 μg/d for women of fertile age, a gap that could be bridged by implementation of folic acid fortification. The results of mandatory folic acid fortifications introduced in USA and Canada are a decrease between 25 and 45% of NTD pregnancies. CONCLUSION: Evidence-based NTD prophylaxis is now practised in more than 60 countries worldwide. EU countries worry over possible cancer risks, but ignore a wealth of studies reporting decreasing cancer risks with folate intakes at recommended levels. Currently, there are indications of a U-shaped relationship, that is, higher cancer risks at low folate intakes (<150 μg/day) and highly elevated folate intakes (>1 mg/day), respectively. However neither the global World Cancer Research review nor EU's European Food Safety Authority report present data on increased cancer risk at physiological folate intake levels. Therefore, EU should act to implement folic acid fortification as NTD prophylaxis as soon as possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".