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Record W2993272889 · doi:10.1542/peds.2019-2461fff

Birth Weight for Gestational Age and the Risk of Asthma in Childhood and Adolescence: A Retrospective Cohort Study

2019· article· en· W2993272889 on OpenAlexaboutno aff
Evelyn Wang, Angela Duff Hogan

Bibliographic record

VenuePEDIATRICS · 2019
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGestational ageBirth weightAsthmaSmall for gestational agePregnancyObstetricsLow birth weightCohortPediatricsRetrospective cohort studyPopulationCohort studyPercentileOverweightBody mass indexEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

To evaluate the possible correlation between birth weight for gestational age and asthma in childhood and adolescents.This was a cohort of all children of a term (≥37 weeks), singleton gestation born in the Canadian province Nova Scotia between January 1, 1989, and December 31, 2014.The retrospective cohort was created through the Nova Scotia Atlee Perinatal Database and with provincial administrative health data. Linkage was conducted by Health Data Nova Scotia on the basis of the health card number. Infants were categorized as small for gestational age (SGA; < 10th percentile), large for gestational age (LGA; >90th percentile), or appropriate for gestational age (10th–90th percentile). Birth weight z scores were calculated by using sex- and gestational age–specific means and SDs from the same reference population. Confounders included the following: maternal age, area of residence (urban versus rural), area-level income quintile at birth, parity, prepregnancy weight status (normal, overweight, or obese), asthma during pregnancy, mode of delivery (vaginal delivery versus cesarean delivery), and smoking in pregnancy (yes or no). Unadjusted and adjusted Cox proportional hazards regression with robust SEs was used to estimate the association between birth weight for gestational age and asthma. Associations between birth weight for gestational age and smoking in pregnancy were also examined.The final sample included 40 727 children. Of those, 10 155 children (23.6%) had asthma by 6 years of life, and 12 997 children (30.2%) had asthma during the first 18 years of life. LGA was not associated with the risk of developing asthma. There was an additive interaction between SGA and smoking. In mothers who smoked, there was an increased risk for asthma in children with low birth weight. However, in mothers who do not smoke, there was no increased risk of asthma across the birth weight spectrum among children.Term infants who are SGA are not at increased risk for asthma in the absence of smoking during pregnancy. There is also no association between LGA and the development of asthma.Infants who are low birth weight are believed to be at increased risk for asthma in childhood, but multiple factors may influence this risk. This cohort has an impressive number of infants managed in a universal health care system, in which asthma diagnosis sensitivity is 89% and specificity is 72% for this population, based on physician diagnosis, not parent recall. This study is unique in that with its regression models it was able to clearly demonstrate the role smoking has on the development of asthma. Prenatal smoking has a significantly stronger negative influence on developing asthma than early postnatal exposures. SGA infants born to smoking mothers are at a significantly increased risk for asthma. It was also interesting that although LGA infants are at risk later for obesity (a risk factor for asthma), they have no increase in asthma. Prevention of childhood asthma is essential to decrease future morbidity and possible mortality. The more risk factors we understand, the better counseling we can provide mothers early in pregnancy. Do not smoke!

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)0.001
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 score0.549
Threshold uncertainty score0.907

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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