Global folate status in women of reproductive age: a systematic review with emphasis on methodological issues
Bibliographic record
Abstract
Inadequate folate status in women of reproductive age (WRA) can lead to adverse health consequences of public health significance, such as megaloblastic anemia (folate deficiency) and an increased risk of neural tube defect (NTD)-affected pregnancies (folate insufficiency). Our review aims to evaluate current data on folate status of WRA. We queried eight databases and the World Health Organization Micronutrients Database, identifying 45 relevant surveys conducted between 2000 and 2014 in 39 countries. Several types of folate assays were used in the analysis of blood folate, and many surveys used folate cutoffs not matched to the assay. To allow better comparisons across surveys, we attempted to account for these differences. The prevalence of folate deficiency was >20% in many countries with lower income economies but was typically <5% in countries with higher income economies. Only 11 surveys reported the prevalence of folate insufficiency, which was >40% in most countries. Overall, folate status data for WRA globally are limited and must be carefully interpreted due to methodological issues. Future surveys would benefit from using the microbiologic assay to assess folate status, along with assay-matched cutoffs to improve monitoring and evaluation of folic acid interventions, thus informing global efforts to prevent NTDs.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".