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Record W4386916005 · doi:10.1136/bmj-2022-072249

Suboptimal gestational weight gain and neonatal outcomes in low and middle income countries: individual participant data meta-analysis

2023· review· en· W4386916005 on OpenAlexfundno aff
Nandita Perumal, Dongqing Wang, Anne Marie Darling, Enju Liu, Molin Wang, Tahmeed Ahmed, Parul Christian, Kathryn G. Dewey, Gilberto Kac, Stephen Kennedy, Vishak Subramoney, Brittany Briggs, Wafaie Fawzi

Bibliographic record

VenueBMJ · 2023
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBill and Melinda Gates Foundation
KeywordsMedicineWeight gainPregnancyBody mass indexMeta-analysisObstetricsConfoundingAnthropometryBirth weightGestational ageProspective cohort studyConfidence intervalRelative riskGestationPediatricsDemographyInternal medicineBody weight

Abstract

fetched live from OpenAlex

Abstract Objective To estimate the associations between gestational weight gain (GWG) during pregnancy and neonatal outcomes in low and middle income countries. Design Individual participant data meta-analysis. Setting Prospective pregnancy studies from 24 low and middle income countries. Main outcome measures Nine neonatal outcomes related to timing (preterm birth) and anthropometry (weight, length, and head circumference) at birth, stillbirths, and neonatal death. Analysis methods A systematic search was conducted in PubMed, Embase, and Web of Science which identified 53 prospective pregnancy studies published after the year 2000 with data on GWG, timing and anthropometry at birth, and neonatal mortality. GWG adequacy was defined as the ratio of the observed maternal weight gain over the recommended weight gain based on the Institute of Medicine body mass index specific guidelines, which are derived from data in high income settings, and the INTERGROWTH-21st GWG standards. Study specific estimates, adjusted for confounders, were generated and then pooled using random effects meta-analysis models. Maternal age and body mass index before pregnancy were examined as potential modifiers of the associations between GWG adequacy and neonatal outcomes. Results Overall, 55% of participants had severely inadequate (<70%) or moderately inadequate (70% to <90%) GWG, 22% had adequate GWG (90-125%), and 23% had excessive GWG (≥125%). Severely inadequate GWG was associated with a higher risk of low birthweight (adjusted relative risk 1.62, 95% confidence interval 1.51 to 1.72; 48 studies, 93 337 participants; τ 2 =0.006), small for gestational age (1.44, 1.36 to 1.54; 51 studies, 93 191 participants; τ 2 =0.016), short for gestational age (1.47, 1.29 to 1.69; 40 studies, 83 827 participants; τ 2 =0.074), and microcephaly (1.57, 1.31 to 1.88; 31 studies, 80 046 participants; τ 2 =0.145) compared with adequate GWG. Excessive GWG was associated with a higher risk of preterm birth (1.22, 1.13 to 1.31; 48 studies, 103 762 participants; τ 2 =0.008), large for gestational age (1.44, 1.33 to 1.57; 47 studies, 90 044 participants; τ 2 =0.009), and macrosomia (1.52, 1.33 to 1.73; 29 studies, 68 138 participants; τ 2 =0) compared with adequate GWG. The direction and magnitude of the associations between GWG adequacy and several neonatal outcomes were modified by maternal age and body mass index before pregnancy. Conclusions Inadequate and excessive GWG are associated with a higher risk of adverse neonatal outcomes across settings. Interventions to promote optimal GWG during pregnancy are likely to reduce the burden of adverse neonatal outcomes, however further research is needed to assess optimal ranges of GWG based on data from low and middle income 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.423
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.366
GPT teacher head0.452
Teacher spread0.086 · 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.

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

Citations78
Published2023
Admission routes1
Has abstractyes

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