Genetic variation in dopamine neurotransmission and motor development of infants born extremely‐low‐birthweight
Bibliographic record
Abstract
AIM: To determine if genetic variation associated with decreased dopamine neurotransmission predicts a decrease in motor development in a convenience cohort study of infants born extremely-low-birthweight (ELBW). METHOD: Four hundred and ninety-eight infants born ELBW had genome-wide genotyping and a neurodevelopmental evaluation at 18 to 22 months of age, corrected for preterm birth. A polygenic risk score (PRS) was created to combine into one predictor variable the hypothesized influences on motor development of alleles at seven independent single nucleotide polymorphisms previously associated with relative decreases in both dopamine neurotransmission and motor learning, by summing the number of alleles present in each infant (range=0-14). The motor development outcome was the Psychomotor Development Index (PDI) of the Bayley Scales of Infant Development, Second Edition. The linear regression models were adjusted for seven clinical and four genetic ancestry covariates. The mean PRS of infants with cerebral palsy (CP) was compared to those without CP. RESULTS: PRS was inversely related to PDI (p=0.011). Each 1-point increase in PRS resulted in an average decrease in PDI of 1.37 points. Patients with CP did not have a greater mean PRS than those without (p=0.67), both with and without adjustment for covariates. INTERPRETATION: Genetic variation that favors a decrease in dopamine neurotransmission predisposes to a decrease in motor development in infants born ELBW, but not to the diagnosis of CP. WHAT THIS PAPER ADDS: Genetic variation in dopamine neurotransmission was associated with a decrease in motor development in infants born at an extremely-low-birthweight. It does not predispose to the diagnosis of cerebral palsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".