Obesity During Pregnancy and Deficits in Offspring Neurobehavioral Flexibility: The CONFINE Model
Bibliographic record
Abstract
Exposure to maternal prenatal obesity is associated with multiple psychiatric and cognitive problems in children. However, it is unclear how and why these children are at risk for such a wide range of difficulties. Prenatal obesity may alter fetal neurodevelopment in ways that shape broad traits that underlie the etiology and symptomatology of the multiple problems observed in children. Novel theoretical frameworks that identify these traits are critical to developing a more complete understanding of how and why prenatal obesity adversely impacts children's psychiatric and cognitive functioning. In this review, we propose the CONFINE (confined offspring neurobehavioral flexibility following intrauterine obesity exposure) model, which posits that prenatal exposure to maternal obesity leads to neurobehavioral flexibility deficits in children. It is argued that prenatal obesity alters the fetal 1) hypothalamic energy balance system in ways that constrain child behavior toward food seeking/consumption; 2) central reward systems (μ opioid and mesocorticolimbic dopamine system), making it difficult for children to disengage from reward-seeking/consuming behaviors; and 3) higher-order salience and cognitive control networks, constraining children's attention toward reward cues and contributing to problems with altering goal-directed behaviors. Together, these changes contribute to neurobehavioral flexibility deficits that, depending on postnatal conditions, may increase children's risk for multiple psychiatric and cognitive problems. The CONFINE model aims to integrate the effects of prenatal obesity on children from neural systems to observed behaviors and enable researchers to develop testable hypotheses to gain a more comprehensive understanding of the associations, mechanisms, and risk trajectories of children prenatally exposed to obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".