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Record W3193989488 · doi:10.1136/bmjgh-2021-005856

Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis

2021· review· en· W3193989488 on OpenAlexaff
Nagendra Monangi, Huan Xu, Rasheda Khanam, Waqasuddin Khan, Saikat Deb, Jesmin Pervin, Joan T. Price, Stephen Kennedy, Yue‐Mei Fan, Thanh Quang Lê, Angharad Care, Julio A. Landero Figueroa, Gerald F. Combs, Elizabeth Belling, Joanne Chappell, Fansheng Kong, Criag Lacher, Salahuddin Ahmed, Nabidul Haque Chowdhury, Sayedur Rahman, Furqan Kabir, Muhammad Imran Nisar, Aneeta Hotwani, Usma Mehmood, Ambreen Nizar, Javairia Khalid, Usha Dhingra, Arup Dutta, Said M. Ali, Fahad Aftab, Mohammed Hamad Juma, Monjur Rahman, Bellington Vwalika, Patrick Musonda, Tahmeed Ahmed, Md Munirul Islam, Kenneth Maleta, Mikko Hallman, Laura Goodfellow, Juhi Gupta, Ana Alfirevic, Susan K. Murphy, Larry Rand, Kelli K. Ryckman, Jeffrey C. Murray, Rajiv Bahl, James A. Litch, Courtney Baruch-Gravett, Žarko Alfirević, Per Ashorn, Abdullah H Baqui, Jane E. Hirst, Cathrine Hoyo, Fyezah Jehan, Laura L. Jelliffe‐Pawlowski, Anisur Rahman, Daniel Roth, Sunil Sazawal, Jeffrey S. A. Stringer, Ge Zhang, Louis J. Muglia

Bibliographic record

VenueBMJ Global Health · 2021
Typereview
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsHospital for Sick Children
FundersDuke Cancer InstituteFHI 360University of California, DavisCincinnati Children's Hospital Medical CenterMarch of Dimes FoundationUniversity of California, San FranciscoWorld Health OrganizationNational Institute of Environmental Health SciencesBurroughs Wellcome FundNational Institute of Diabetes and Digestive and Kidney DiseasesMarch of Dimes Prematurity Research Center Ohio CollaborativeBill and Melinda Gates FoundationUnited States Agency for International DevelopmentU.S. Environmental Protection Agency
KeywordsMedicineGestational ageMeta-analysisPregnancyGestationObstetricsCohort studyInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations. METHODS: Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis. FINDINGS: In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi. INTERPRETATION: While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

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)0.017
metaresearch head score (Gemma)0.029
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 score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.029
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.048
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
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 head0.089
GPT teacher head0.421
Teacher spread0.332 · 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

Citations31
Published2021
Admission routes1
Has abstractyes

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