An initial investigation of neonatal neuroanatomy, caregiving, and levels of disorganized behavior
Bibliographic record
Abstract
Attachment disorganization is a risk factor for difficulties in attention, social relationships, and mental health. Conceptually, attachment disorganization may indicate a breakdown in fear regulation resulting from repeated exposure to frightening maternal care. In addition, past research has examined the influence of stress-inducing contextual factors and/or child factors upon the development of disorganization. However, no past work has assessed whether infant neuroanatomy, important to stress regulation, moderates the association between maternal care and levels of disorganized behavior. Here, utilizing data from a subsample of 82 dyads taking part in the "Growing Up in Singapore towards Healthy Outcomes" (GUSTO) cohort, we assessed the prediction from maternal sensitive caregiving at 6 mo and levels of attachment disorganization at 1.5 y, as moderated by hippocampal and amygdala volume determined within the first 2 weeks of life. Results indicate a significant interaction between neonatal left hippocampal volume and maternal sensitivity upon levels of disorganized behavior. Although these results require substantiation in further research, if replicated, they may enable new strategies for the identification of processes important to child mental health and points for intervention. This is because neonatal neuroanatomy, as opposed to genetic variation and sociodemographic risk, may be more directly linked to stress responses within individuals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".