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Record W4378903074 · doi:10.1186/s12889-023-15913-3

Birthweight, gestational age, and early school trajectory

2023· article· en· W4378903074 on OpenAlexafffund
Rabi Joël Gansaonré, Lynne Moore, Jean‐François Kobiané, Ali Sié, Slim Haddad

Bibliographic record

VenueBMC Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleHôpital de l'Enfant-JésusUniversité Laval
FundersUniversité Laval
KeywordsMedicineGestational ageDemographyPoisson regressionPediatricsIncidence (geometry)BiostatisticsSmall for gestational ageDropout (neural networks)EpidemiologyGerontologyPopulationPregnancyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Birthweight and gestational age are important factors of not only newborn health by also child development and can contribute to delayed cognitive abilities. However, no study has analyzed the association of birthweight and gestational age with school trajectory measured simultaneously by school entry, grade repetition, and school dropout. This study aims, first, to analyze the association of birthweight or gestational age with school entry, and second, to explore the relationship between birthweight or gestational age and grade repetition and school dropout among children in Ouagadougou, Burkina Faso. METHODS: This study used longitudinal data from the Ouagadougou Health and Demographic Surveillance System. Our samples consisted of children born between 2008 and 2014 who were at least three years old at the beginning of the 2017-18 school year. Samples included 13,676, 3152, and 3498 children for the analysis of the school entry, grade repetition, and dropout, respectively. A discrete-time survival model was used to examine the relationship between birthweight or gestational age and school entry, grade repetition, and dropout. The association between birthweight or gestational age and age at school entry were assessed using a Poisson regression. RESULTS: The incidence rate of school entry was 18.1 per 100 people-years. The incidence of first repetition and dropout were 12.6 and 5.9, respectively. The probability of school entry decreased by 31% (HR:0.69, 95%CI: 0.56-0.85) and 8% (HR:0.92, 95%CI: 0.85-0.99) for children weighing less than 2000 g and those weighing between 2000 and 2499 g, respectively, compared to those born with a normal weight (weight ≥ 2500 g). The age at school entry of children with a birthweight less than 2000 g and between 2000 and 2499 g was 7% (IRR: 1.07, 95%CI: 1.06-1.08) and 3% (IRR: 1.03, 95%CI: 1.00-1.06) higher than children born at a normal birthweight, respectively. Gestational age was not associated with school entry or age at school entry. Similarly, birthweight and gestational age were not associated with grade repetition or dropout. CONCLUSION: This study shows that low birthweight is negatively associated with school entry and age at school entry in Ouagadougou. Efforts to avoid low birthweights should be part of maternal and prenatal health care because the associated difficulties may be difficult to overcome later in the child's life. Further longitudinal studies are needed to better understand the relationship between development at birth and school trajectory.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.317
Teacher spread0.261 · 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 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

Citations4
Published2023
Admission routes2
Has abstractyes

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