Trends of selected malformations in relation to folic acid recommendations and fortification: An international assessment
Bibliographic record
Abstract
BACKGROUND: Two crucial issues relative to the benefits and impact of folic acid in the prevention of birth defects are whether supplementation recommendations alone, without fortification, are effective in reducing the population-wide rates of neural tube defects (NTDs), and whether such policies can reduce the occurrence of other birth defects. Using data from 15 registries, we assessed rates and trends of 14 major defects, including NTDs, in areas with official recommendations or fortification to assess the effectiveness of recommendations and fortification on a wide range of major birth defects. METHODS: We evaluated surveillance data through 2003 on major birth defects from population-based registries from Europe, North America, and Australia. All included ascertainment of pregnancy terminations (where legal). Trends before and after policies or fortification were assessed via Poisson regression and were compared via rate ratios. RESULTS: Significant changes in trends were seen for NTDs in areas with fortification but not in areas with supplementation recommendations alone. For other major birth defects, there was an overall lack of major trend changes after recommendations or fortification. However, some significant declines were observed for select birth defects in individual areas. CONCLUSIONS: Recommendations alone remain an ineffective approach in translating the known protective effect of folic acid in population-wide decline in NTD rates. Fortification appears to be effective in reducing NTDs. The effect on other birth defects remains unclear.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".