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Record W4296702913 · doi:10.1093/ajcn/nqac259

Effects of prenatal nutritional supplements on gestational weight gain in low- and middle-income countries: a meta-analysis of individual participant data

2022· review· en· W4296702913 on OpenAlexfundno aff
Enju Liu, Dongqing Wang, Anne Marie Darling, Nandita Perumal, Molin Wang, Tahmeed Ahmed, Parul Christian, Kathryn G. Dewey, Gilberto Kac, Stephen Kennedy, Vishak Subramoney, Brittany Briggs, Wafaie Fawzi

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2022
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersYale Institute for Global Health, Yale School of MedicineUniversity of Colorado School of Medicine, Anschutz Medical CampusJohns Hopkins Bloomberg School of Public HealthFaculty of Medicine and Health, University of SydneyUniversity of Maryland School of Public HealthTaysSchool of Public Health and Family Medicine, University of Cape TownUniversiteit GentRollins School of Public HealthNational Institutes of HealthMuhimbili University of Health and Allied SciencesHospital for Sick ChildrenBill and Melinda Gates FoundationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of GhanaWorld Wildlife FundHarvard T.H. Chan School of Public HealthBacha Khan UniversityEmory UniversityNational Department of HealthMedical Research CouncilRTI InternationalUniversity of Health and Allied SciencesJohns Hopkins UniversityKing's College London
KeywordsMedicineWeight gainObstetricsMeta-analysisPregnancyGestationGynecologyInternal medicineBody weight

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0130.032
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.272
GPT teacher head0.480
Teacher spread0.208 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations20
Published2022
Admission routes1
Has abstractno

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