Improving maternal folate status to prevent infant neural tube defects: working group conclusions and a framework for action
Bibliographic record
Abstract
As infectious disease control programs achieve increasing success, further reductions in child mortality in low- and middle-income countries (LMICs) will require focused prevention strategies for birth defects and other noninfectious diseases. Neural tube defects (NTDs) can cause early death or lifelong disability. Preventing NTDs provides a feasible, significant opportunity to decrease the toll of birth defects and contribute to further reducing child mortality globally. The Micronutrient Forum convened a technical consultation on Folate Status in Women and Neural Tube Defects Prevention to develop a roadmap to inform and prioritize investments in NTD prevention in LMICs; help guide implementation efforts in terms of the feasibility of interventions and the potential for acceleration; and identify research and knowledge gaps. Here, we describe the impetus for and approach to the consultation and present the conclusions and a framework for developing a roadmap for action to accelerate NTD prevention in LMICs. The framework (1) provides options for action on folate status assessment; (2) outlines a way forward to develop and implement a time-bound global action plan for NTD prevention; and (3) identifies common impediments to NTD prevention, broad strategies to overcome or minimize these impediments, and basic building blocks necessary to accelerate action.
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 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 | 0.097 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".