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Record W2052690650 · doi:10.1542/peds.2007-0045

Predicting Language at 2 Years of Age: A Prospective Community Study

2007· article· en· W2052690650 on OpenAlexaff
Sheena Reilly, Melissa Wake, Edith L. Bavin, Margot Prior, Joanne Williams, Lesley Bretherton, Patricia Eadie, Yin Barrett, Obioha C. Ukoumunne

Bibliographic record

VenuePEDIATRICS · 2007
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsPolicyWise for Children & Families
FundersNational Medical Research CouncilNational Health and Medical Research Council
KeywordsMedicineBirth orderLogistic regressionDemographySocioeconomic statusVocabularyBirth weightLanguage developmentFamily historyDevelopmental psychologyPregnancyPopulationPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: This article responds to evidence gaps regarding language impairment identified by the US Preventive Services Task Force in 2006. We examine the contributions of putative child, family, and environmental risk factors to language outcomes at 24 months of age. METHODS: A community-ascertained sample of 1720 infants who were recruited at 8 months of age were followed at ages 12 and 24 months in a prospective, longitudinal study in metropolitan Melbourne, Australia. Outcomes at 24 months were parent-reported infant communication (Communication and Symbolic Behavior Scales and MacArthur-Bates Communicative Development Inventories vocabulary production score). Putative risk factors were gender, preterm birth, birth weight, multiple birth, birth order, socioeconomic status, maternal mental health, maternal vocabulary and education, maternal age at birth of child, non-English-speaking background, and family history of speech-language difficulties. Linear regression models were fitted to total standardized Communication and Symbolic Behavior Scales and Communicative Development Inventories vocabulary production scores; a logistic regression model was fitted to late-talking status at 24 months. RESULTS: The regression models accounted for 4.3% and 7.0% of the variation in the 24-month Communication and Symbolic Behavior Scales and Communicative Development Inventories scores, respectively. Male gender and family history were strongly associated with poorer outcomes on both instruments. Lower Communication and Symbolic Behavior Scales scores were also associated with lower maternal vocabulary and older maternal age. Lower vocabulary production scores were associated with birth order and non-English-speaking background. When the 12-month Communication and Symbolic Behavior Scales Total score was added as a covariate in the linear regression of 24-month Communication and Symbolic Behavior Scales Total score, it was by far the strongest predictor. CONCLUSIONS: These early risk factors explained no more than 7% of the variation in language at 24 months. They seem unlikely to be helpful in screening for early language delay.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.292
Teacher spread0.276 · 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

Citations238
Published2007
Admission routes1
Has abstractyes

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