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Predicting IQ of Biologically “At Risk” Children from Age 3 to School Entry

2000· article· en· W2030177867 on OpenAlexaff
Ruth E. Grunau, Michael F. Whitfield, Julianne Petrie

Bibliographic record

VenueJournal of Developmental & Behavioral Pediatrics · 2000
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsRaw scoreWechsler Adult Intelligence ScaleIntelligence quotientWechsler Intelligence Scale for ChildrenPsychologyPredictive validityWechsler Preschool and Primary Scale of IntelligenceTest scoreCognitionDevelopmental psychologyPediatricsClinical psychologyMedicineStandardized testPsychiatryStatisticsRaw data

Abstract

fetched live from OpenAlex

Predictive validity of the Stanford-Binet Intelligence Scale Fourth Edition (S-B IV) from age 3 years to ages 4-5 years was evaluated with biologically "at risk" children without major sensory or motor impairments (n = 236). Using the standard scoring, children with full scale IQ < or = 84 on the Wechsler Preschool and Primary Scale of Intelligence at age 4-5 years were poorly identified (sensitivity 54%) from the composite S-B IV score at age 3. However, sensitivity improved greatly to 78% by including as a predictor the number of subtests the child was actually able to perform at age 3 years. Measures from the Home Screening Questionnaire and ratings of mother-child interaction further improved sensitivity to 83%. The standard method for calculating the composite score on the S-B IV excludes subtests with a raw score of 0, which overestimates cognitive functioning in young biologically high risk children. Accuracy of early identification was improved significantly by considering the number of subtests the child did not perform at age 3 years.

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)0.007
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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

Citations9
Published2000
Admission routes1
Has abstractyes

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