Executive Function in Children Born Moderate-to-Late Preterm: A Meta-Analysis
Bibliographic record
Abstract
CONTEXT: The risk of early neurodevelopmental delay is increasingly recognized in children born moderate-to-late preterm (MLP; 32-36 weeks' gestation), but school-aged cognitive outcomes are unclear, particularly for domains such as executive function (EF). OBJECTIVE: To evaluate EF outcomes (attentional control, cognitive flexibility, and goal setting) in school-aged children born MLP compared with children born at term. DATA SOURCES: Medline, Embase, PsycInfo, and Scopus. STUDY SELECTION: Studies assessing EF outcomes (overall EF, attentional control, cognitive flexibility, and goal setting) in children born MLP aged between 6 and 17 years, which included a term-born control group. DATA EXTRACTION: Two reviewers screened for eligibility and completed the risk of bias assessment using the Newcastle-Ottawa Scale, and 1 reviewer extracted data. Random effects meta-analyses were performed. RESULTS: Twelve studies were eligible for inclusion in the meta-analyses (2348 MLP children and 20 322 controls). Children born MLP had poorer overall EF compared with children born at term (standardized mean difference, -0.15, 95% confidence interval, -0.21 to -0.09; P < .0001; I2 = 47.59%). Similar conclusions were noted across the subdomains of attentional control, cognitive flexibility, and goal setting. LIMITATIONS: Study methodologies and EF measures varied. Only a small number of studies met eligibility criteria and were from developed countries. CONCLUSIONS: School-aged children born MLP may experience greater challenges in EF compared with term-born children. Further research is needed to investigate the potential impact these challenges have on functional outcomes such as academic achievement and social-emotional functioning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".