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Record W2763012474 · doi:10.1093/pch/20.5.e54a

59: Low Socioeconomic Status and Very Preterm Birth: A “Double Jeopardy” in Child Language Development

2015· article· en· W2763012474 on OpenAlexaff
A Chomyn, Amber Reichert, Linda Carroll, Mosarrat Qureshi, Jennifer Toye

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSocioeconomic statusMedicineGestational agePediatricsPopulationLogistic regressionDisadvantagedLanguage developmentDemographyPregnancyDevelopmental psychologyPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

As neonatal care advances, an increasing number of infants survive the immediate complications of preterm birth. In addition to ongoing physical health problems associated with preterm birth, these babies are more prone to delays in multiple domains of child development including cognitive, behavioral, and language development. Delay in these domains may impact future success in both academic and social endeavors. Compounding the issue is that preterm birth is more common, and increasing most rapidly, in low socioeconomic populations- a group already disadvantaged in the same domains of child development. In our study, we look to establish if very preterm birth and living in an urban area of low socioeconomic status (SES) at time of birth negatively influences language development in infants at 18 months adjusted age and 36 months. Using combined data from our local Neonatal Database and Neonatal Follow Up Database, we assessed the relationship between socioeconomic status and language outcomes in very preterm babies by comparing scores on validated language tools, administered during neonatal follow up care between SES groups. Univariate analysis and multivariable logistic regression was performed to evaluate the association between low SES and language development. Maternal and infant characteristics were compared between low SES, and combined average and high SES babies. Of the characteristics assessed, significant differences existed in maternal age, diabetes, and gestational age. Mortality and major neonatal morbidities associated with prematurity were compared between groups, and no association was found with SES. No significant difference in language outcomes were observed in our population at 18 months adjusted age, however by 36 months low SES babies had significantly more delayed composite language scores compared to average/high SES babies (odds ratio = 4.9, p-value= 0.01). There was a trend demonstrated towards poorer performance in both expressive and receptive language assessment compared to average and high SES babies. In our setting, low SES is associated with greater language delay in very preterm babies by 36 months. Further study is needed to assess if this delay is associated with school readiness, or other outcomes later in life. These results support prospective studies to evaluate the success of multidisciplinary interventional programs to modify the social risk and outcomes in low SES populations.

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)0.003
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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
Published2015
Admission routes1
Has abstractyes

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