MétaCan
Menu
Back to cohort

Effect of High-dose Folic Acid Supplementation in Pregnancy on Preeclampsia (FACT): Double-blind, Phase III, Randomized Controlled, International, Multicenter Trial

2019· article· en· W2945980893 on OpenAlexaff
Sheng Wen, Ruth Rennicks White, Natalie Rybak, Laura Gaudet, Stephen C. Robson, W. Hague, Donnette Simms‐Stewart, G. Carroli, Gary Smith, W.D. Fraser, George A. Wells, Sandra T. Davidge, J.C.P. Kingdom, Doug Coyle, David M. Fergusson, Daniel J. Corsi, Josée Champagne, Elham Sabri, Tim Ramsay, B. W. J. Mol, Martijn A. Oudijk, Melissa Walker

Bibliographic record

VenueObstetric Anesthesia Digest · 2019
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicinePreeclampsiaFolic acidFirst trimesterPregnancyFolic acid supplementationObstetricsIncidence (geometry)Third trimesterNeural tubeRandomized controlled trialFetusInternal medicine

Abstract

fetched live from OpenAlex

(BMJ. 2018;362:k3478) Preeclampsia is a leading cause of perinatal morbidity and mortality, and causes >35,000 maternal deaths worldwide each year. While studies of the association between folic acid supplementation and the incidence of preeclampsia have yielded inconsistent results, a potential protective effect has been shown. Worldwide, folic acid supplementation has been recommended during the preconception period through the first trimester for the prevention of neural tube defects. Preeclampsia, however, has 2 stages: 1 in the late first trimester and another in the third trimester. This study aims to evaluate the effect of daily high-dose (4.0 mg) folic acid supplementation beyond the first trimester on the risk of preeclampsia in women with identified risk factors.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.315
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueObstetric Anesthesia DigestSame topicPregnancy and preeclampsia studiesFrench-language works237,207