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Record W3207394396 · doi:10.1002/icd.2275

Longitudinal assessment of social cognition in infants born preterm using eye‐tracking and parent–child play

2021· article· en· W3207394396 on OpenAlexfundno aff
Bethan Dean, Sinéad O’Carroll, Lorna Ginnell, Victoria Ledsham, Emma J. Telford, Sarah Sparrow, James P. Boardman, Sue Fletcher‐Watson

Bibliographic record

VenueInfant and Child Development · 2021
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersMedical Research Council Canada
KeywordsPsychologyDevelopmental psychologyEye trackingJoint attentionLongitudinal studyTracking (education)CognitionCohortSocial relationAutismMedicineSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Preterm birth is associated with reduced social attention in infancy. Are these early social attention differences linked to later interactive ability? This study draws on a well‐characterized preterm cohort in whom we have previously demonstrated a reduced attentional preference for social information in infancy, using eye‐tracking. States of engagement during parent–child play at 60 months were coded for 36 preterm‐ and 31 term‐born children. We also repeated the eye‐tracking assessment of social attention previously performed in infancy and evaluated neurodevelopment via the Mullen Scales of Early Learning. Children born preterm or at term spent similar percentages of time in different engagement states. Infant and child social attentional profiles did not relate to the complexity of engagement. Preterm infants' language impairments correlated with time spent in conversational joint engagement. Children born preterm showed complex social interaction abilities unrelated to their profile of social attention in infancy.

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 score0.012
Threshold uncertainty score0.024

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.315
Teacher spread0.289 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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