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Record W3137966472 · doi:10.1093/aje/kwab061

Food Fortification With Folic Acid for Prevention of Spina Bifida and Anencephaly: The Need for a Paradigm Shift in Evidence Evaluation for Policy-Making

2021· article· en· W3137966472 on OpenAlexaff
Homero Martı́nez, Helena Pachón, Vijaya Kancherla, Godfrey P. Oakley

Bibliographic record

VenueAmerican Journal of Epidemiology · 2021
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsNutrition International
FundersHospital Infantil de México Federico GómezEmory University
KeywordsSpina bifidaMedicineContext (archaeology)AnencephalyPublic healthEnvironmental healthObservational studyFolic acidLegislationGerontologyPediatricsPregnancyPolitical scienceNursingPathologyInternal medicineLaw

Abstract

fetched live from OpenAlex

Context-specific evidence evaluation is advocated in modern epidemiology to support public health policy decisions, avoiding excessive reliance on experimental study designs. Here we present the rationale for a paradigm shift in evaluation of the evidence derived from independent studies, as well as systematic reviews and meta-analyses of observational studies, applying Hill's criteria (including coherence, plausibility, temporality, consistency, magnitude of effect, and dose-response) to evaluate food fortification as an effective public health intervention against folic acid-preventable (FAP) spina bifida and anencephaly (SBA). A critical appraisal of evidence published between 1983 and 2020 supports the conclusion that food fortification with folic acid prevents FAP SBA. Policy-makers should be confident that with mandatory legislation, effective implementation, and periodic evaluation, food fortification assures that women of reproductive age will safely receive daily folic acid to significantly reduce the risk of FAP SBA. Current evidence should suffice to generate the political will to implement programs that will save thousands of lives each year in over 100 countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.160
GPT teacher head0.471
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2021
Admission routes1
Has abstractyes

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