Developing a Global Strategy for the Control of Folate Deficiency and Folic Acid Responsive Neural Tube Defects in Low- and Middle-Income Countries (P10-107-19)
Bibliographic record
Abstract
To develop a global action plan for the control of folate deficiency and folic acid responsive neural tube defects (anencephaly and spina bifida) Establish a multi- and inter-disciplinary group to develop a global action plan for folate-sensitive NTD prevention Ensure regional lab capacity exists to assess folate status applying a harmonized microbiologic assay (MBA) to measure red blood cell (RBC) folate, establishing a global network of regional laboratories coordinated by an umbrella organization Promote improvements of folate status in women of reproductive age (WRA) in LMIC, engaging a wide variety of national and global stakeholders Address key knowledge gaps related to the prevention of folate sensitive NTDs Improve knowledge availability and sharing amongst global stakeholders in NTD prevention Communicate and share the work of the Folate Task Team A standing Folate Task Team comprised of a 2-member Secretariat, a 10-member Expert Advisory Group, 4 Ex-Officio members, and 4 “As Needed” advisors (see Fig. 1) Identification of an initial list of 12 global stakeholders and partner organizations Five resource laboratories have been trained at the Division of Laboratory Sciences - CDC, including labs in Vietnam, Sri Lanka, Philippines, Tanzania, and Pakistan A training video supported by a manual and mini posters to illustrate specific activities of the assay has been produced by CDC. A landscaping analysis has identified countries that have mandatory/voluntary food fortification practices, information on folate status in the population, burden of NTDs, and consumption patterns of fortified foods Identification of alternative foods/food vehicles likely to be fortified to reach at-risk segments of WRA Dissemination activities are shared at https://www.nutritionintl.org/what-we-do/nteam/folate-task-team/ The estimated global prevalence of NTD-affected pregnancies is estimated to be 260,100 in 2015 and has a significant emotional and economic impact on families and society, while contributing to the loss of human potential for countries The global action provides a clear path forward to help direct and prioritize investments, advance resource mobilization, and garner the political will to accelerate NTD prevention in LMIC The Bill & Melinda Gates Foundation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".