Data from: Exploratory study evaluating the relationships between perinatal adversity, oxidative stress, and infant neurodevelopment across the first year of life data
Bibliographic record
Abstract
Early childhood adversity increases risk for negative lifelong impacts on health and wellbeing. Identifying the risk factors and the associated biological adaptations early in life is critical to develop scalable early screening tools and interventions. Currently, there are limited, reliable early childhood adversity measures that can be deployed prospectively, at scale, to assess risk in pediatric settings. The goal of this two-site study was to determine if the gold standard measure of oxidative stress, F2-Isoprostanes, is potentially a reliable measure of a physiological response to adversity of the infant and mother. The study evaluated the independent relationships between F2-Isoprostanes, perinatal adversity and infant neurocognitive development. The study included mother-infant dyads born >36 weeks’ gestation. Maternal demographic information and mental health assessments were utilized to generate a perinatal cumulative risk score. Infants’ development was assessed at 6 and 12 months and both mothers and infants were assayed for F2-isoprostane levels in blood and urine, respectively. Statistical analysis revealed that cumulative risk scores correlated with higher maternal (p=0.01) and infant (p=0.05) F2-isoprostane levels at 6 months. Infant F2-isoprostane measures at 2 months were negatively associated with Mullen Scales of Early Learning Composite scores at 12 months (p=0.04). Lastly, higher cumulative risk scores predicted higher average maternal F2-isoprostane levels across the 1 year study time period (p=0.04).The relationship between perinatal cumulative risk scores and higher maternal and infant F2-isoprostanes at 6 months may reflect an oxidative stress status that informs a sensitive period in which a biomarker can be utilized prospectively to reveal the physiological impact of early adversity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".