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Record W2889077785 · doi:10.1136/bmjopen-2017-020410

Prevalence of low birth weight and its association with maternal body weight status in selected countries in Africa: a cross-sectional study

2018· article· en· W2889077785 on OpenAlexaff
Zhifei He, Ghose Bishwajit, Sanni Yaya, Zhaohui Cheng, Dongsheng Zou, Yan Zhou

Bibliographic record

VenueBMJ Open · 2018
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Ottawa
FundersNational Social Science Fund of China
KeywordsUnderweightMedicineLow birth weightChildbirthDemographyBirth weightBody mass indexCross-sectional studyPregnancyDeveloping countryEnvironmental healthObstetricsOverweight

Abstract

fetched live from OpenAlex

OBJECTIVES: The present study aimed to estimate the prevalence of low birth weight (LBW), and to investigate the association between maternal body weight measured in terms of body mass index (BMI) and birth weight in selected countries in Africa. SETTING: Urban and rural household in Burkina Faso, Ghana, Malawi, Senegal and Uganda. PARTICIPANTS: Mothers (n=11 418) aged between 15 and 49 years with a history of childbirth in the last 5 years. RESULTS: The prevalence of LBW in Burkina Faso, Ghana, Malawi, Senegal and Uganda was, respectively, 13.4%, 10.2%, 12.1%, 15.7% and 10%. Compared with women who are of normal weight, underweight mothers had a higher likelihood of giving birth to LBW babies in all countries except Ghana. However, the association between maternal BMI and birth weight was found to be statistically significant for Senegal only (OR=1.961 (95% CI 1.259 to 3.055)). CONCLUSION: Underweight mothers in Senegal share a greater risk of having LBW babies compared with their normal-weight counterparts. Programmes targeting to address infant mortality should focus on promoting nutritional status among women of childbearing age. Longitudinal studies are required to better elucidate the causal nature of the relationship between maternal underweight and LBW.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.358
Teacher spread0.331 · 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 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

Citations113
Published2018
Admission routes1
Has abstractyes

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