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Record W2076653091 · doi:10.1371/journal.pone.0036502

Executive Functions of Six-Year-Old Boys with Normal Birth Weight and Gestational Age

2012· article· en· W2076653091 on OpenAlexaff
Desiree Y. Phua, Anne Rifkin‐Graboi, Seang‐Mei Saw, Michael J. Meaney, Anqi Qiu

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsDouglas Mental Health University InstituteMcGill University
Fundersnot available
KeywordsGestational ageBirth weightMedicineYoung adultPediatricsDemographyObstetricsGerontologyPregnancyBiologyGenetics

Abstract

fetched live from OpenAlex

Impaired fetal development, reflected by low birth weight or prematurity, predicts an increased risk for psychopathology, especially attention deficit hyperactivity disorder (ADHD). Such effects cut across the normal range of birth weight and gestation. Despite the strength of existing epidemiological data, cognitive pathways that link fetal development to mental health are largely unknown. In this study we examined the relation of birth weight (>2500 g) and gestational age (37-41 weeks) within the normal range with specific executive functions in 195 Singaporean six-year-old boys of Chinese ethnicity. Birth weight adjusted for gestational age was used as indicator of fetal growth while gestational age was indicative of fetal maturity. Linear regression revealed that increased fetal growth within the normal range is associated with an improved ability to learn rules during the intra/extra-dimensional shift task and to retain visual information for short period of time during the delayed matching to sample task. Moreover, faster and consistent reaction times during the stop-signal task were observed among boys born at term, but with higher gestational age. Hence, even among boys born at term with normal birth weight, variations in fetal growth and maturity showed distinct effects on specific executive functions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.214
Teacher spread0.192 · 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 teacher head, 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

Citations28
Published2012
Admission routes1
Has abstractyes

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