Folic acid supplementation for the prevention of neural tube defects: promotion and use
Bibliographic record
Abstract
Haydi Al-Wassia1, Prakesh S Shah1,2,3,41Department of Pediatrics, Mount Sinai Hospital, Toronto, Canada; 2Department of Pediatrics, Division of Neonatology, University of Toronto, Canada; 3Mother-Infant Care Research Center, Mount Sinai Hospital, Toronto, Canada; 4Department of Health Policy, Management and Evaluations, University of Toronto, CanadaAbstract: Observational and randomized controlled studies have shown that periconceptional folic acid (FA) supplementation can significantly reduce the risk of neural tube defects (NTDs). Countries across the world have adopted various strategies to increase awareness and to promote the use of FA. Nevertheless, health promotion and educational campaigns have proven to be ineffective in achieving the goal of increasing FA intake by the at-risk group. Mandatory FA fortification was a further step taken by some countries on the course toward improving folate status in the general population. Although some researchers advocate for extra folate to be added to the food supply, a number of governments have refrained from adopting the policy of mandatory fortification because of concerns raised over the potential side effects, such as cancer risk; however, epidemiological confirmation is inconsistent. After several years of the proven association between prenatal supplementation of FA and prevention of NTD, uncertainty, controversy, and indecision still hinder FA promotion and use. In this review, we summarize approaches taken by various countries and provide a framework for further steps in this area.Keywords: folic acid, fortification, neural tube defect
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".