Evidence for discrete profiles of children’s physiological activity across three neurobiological system and their transitions over time
Bibliographic record
Abstract
The conceptualization of stress-responsive physiological systems as operating in an integrated manner is evident in several theoretical models of cross-system functioning. However, limited empirical research has modeled the complexity of multisystem activity. Moreover few studies have explored developmentally regulated changes in multisystem activity during early childhood when plasticity is particularly pronounced. The current study used latent profile analysis (LPA) to evaluate multisystem activity during fall and spring of children's transition to kindergarten in three biological systems: the parasympathetic nervous system (PNS), sympathetic nervous system (SNS), and hypothalamic pituitary adrenal (HPA) axis. Latent transition analysis (LTA) was then used to examine the stability of profile classification across time. Across both timepoints, three distinct profiles of multisystem activity emerged. One profile was characterized by heightened HPA axis activity (HPA Axis Responders), a second profile was characterized by moderate, typically adaptive patterns across the PNS, SNS, and HPA axis (Active Copers/Mobilizers), and a third profile was characterized by heightened baseline activity, particularly in the PNS and SNS (Anticipatory Arousal/ANS Responders). LTA of fall-to-spring profile classifications indicated higher probabilities that children remained in the same profile over time compared to probabilities of profile changes, suggesting stability in certain patterns of cross-system responsivity. Patterns of profile stability and change were associated with socioemotional outcomes at the end of the school year. Findings highlight the utility of LPA and LTA to detect meaningful patterns of complex multisystem physiological activity across three systems and their associations with early adjustment during an important developmental transition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".