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Record W4408283021 · doi:10.1016/j.pedneo.2025.01.010

Motor skills as early indicators for cognitive development in preterm infants with very low birth weight

2025· article· en· W4408283021 on OpenAlexaff
Yen‐Ting Chen, Sot-Fu Lei, Carolyn Tang, Hsiu‐Man Lin, Yaohang Weng, Kai‐Cheng Hsu, Yen‐Tzu Wu, Yung‐Ting Kuo

Bibliographic record

VenuePediatrics & Neonatology · 2025
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsChild, Adolescent and Family Mental Health
FundersChina Medical University HospitalDepartment of Medical Research, Union of Myanmar
KeywordsMotor skillLow birth weightDevelopmental psychologyCognitionGross motor skillPediatricsMedicineCognitive developmentPsychologyAudiologyPsychiatryBiologyPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Preterm babies born with very low birth weight (VLBW, birth weight <1500 g) have inferior long-term neurodevelopmental outcomes to term babies. This study aimed to identify early predictive neurodevelopmental factors for future cognitive outcomes that could serve as indicators for early intervention strategies. METHODS: This longitudinal cohort study enrolled 146 VLBW preterm infants, identified between 2011 and 2020. Each child underwent four neurodevelopmental assessments (at ages 6,12, 24, and 60 months) using the Bayley-III and Wechsler Preschool and Primary Scale of Intelligence-IV examinations. Correlation and linear regression analyses were performed to determine the correlation between early neurodevelopmental status and late cognitive outcomes. We concurrently considered neonatal medical complications and socioeconomic variables as risk factors to develop a prediction model of cognitive outcomes at five years old. RESULTS: A total of 146 VLBW children, born with a mean weight of 1090.4 ± 229.6 g and a mean gestational age of 28.2 ± 2.0 weeks, were evaluated. At 6 months of age, motor outcome was the only factor that exhibited a significant correlation with cognitive development at 5 years of age (p < 0.01, r = 0.242). The strength of the correlation between motor and cognitive function increased with age, reaching greater significance at 12 and 24 months (p < 0.001, r = 0.409 and 0.472, respectively). The linear regression model demonstrated that neonatal medical conditions and Bayley motor score at six months old predicted 26% of the variance in the Full-Scale Intelligence Quotient (FSIQ) at five years old. CONCLUSION: The results of the present study show that motor function was the earliest and persistent predictor of FSIQ. This underscores the importance of prioritizing motor development in interventions as early as six months of age, which could substantially advance the timing of early intervention programs.

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)0.006
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.245
Teacher spread0.241 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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