Birth weight interacts with a functional variant of the oxytocin receptor gene (<i>OXTR</i>) to predict executive functioning in children
Bibliographic record
Abstract
Genetic variation in the oxytocin receptor gene (OXTR) is associated with several psychiatric conditions characterized by deficits in executive functioning (EF). A specific OXTR variant, rs2254298, has previously been associated with brain functioning in regions implicated in EF. Moreover, birth weight variation across the entire range is associated with individual differences in cortical structure and function that underlie EF. This is the first study to examine the main and interactive effect between rs2254298 and birth weight on EF in children. The sample consisted of 310 children from an ongoing longitudinal study. EF was measured at age 4.5 using observational tasks indexing working memory, cognitive flexibility, and inhibitory control. A family-based design that controlled for population admixture, stratification, and nongenomic confounds was employed. A significant genetic association between rs2254298 and EF was observed, with more copies of the major allele (G) associated with higher EF. There was also a significant interaction between rs2254298 and birth weight, such that more copies of the major allele in combination with higher birth weight predicted better EF. Findings suggest that OXTR may be associated with discrete neurocognitive abilities in childhood, and these effects may be modulated by intrauterine conditions related to fetal growth and development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".