Neural tube defect rates before and after food fortification with folic acid
Bibliographic record
Abstract
BACKGROUND: Since 1998, enriched cereal grains sold in the United States have been fortified with folic acid, to reduce the incidence of neural tube defects (NTDs). The Centers for Disease Control and Prevention (CDC) recently reported that NTD rates have decreased 26% since fortification, but that additional effort is needed to achieve the national goal of a 50% reduction. However, accurate determination of NTD rates requires counting antenatally detected cases; the CDC study noted that the number of prenatally diagnosed cases was likely underestimated. METHODS AND RESULTS: We examined studies from the United States and Canada that compared rates of NTDs before and after very similar fortification programs were instituted in each country. U.S. studies had incomplete ascertainment of prenatally diagnosed NTD cases, and as a result, underreported the number of NTDs prevented. Canadian studies, in which ascertainment was more complete, showed decreases in NTD rates up to 54%. CONCLUSIONS: There is a strong correlation between the completeness of ascertainment and the percentage decrease in NTD rates. Studies that identify cases best show that folic acid fortification is preventing around 50% of NTDs. The percentage of NTDs that are folate-preventable in the United States is uncertain, but is probably 50-60%. Thus, we may be quite close to achieving the optimum level of protection at current fortification levels.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".