The mentalizing network and theory of mind mediate adjustment after childhood traumatic brain injury
Bibliographic record
Abstract
Childhood traumatic brain injury (TBI) affects over 600 000 children per year in the United States. Following TBI, children are vulnerable to deficits in psychosocial adjustment and neurocognition, including social cognition, which persist long-term. They are also susceptible to direct and secondary damage to related brain networks. In this study, we examine whether brain morphometry of the mentalizing network (MN) and theory of mind (ToM; one component of social cognition) mediates the effects of TBI on adjustment. Children with severe TBI (n = 15, Mage = 10.32), complicated mild/moderate TBI (n = 30, Mage = 10.81) and orthopedic injury (OI; n = 42, Mage = 10.65) completed measures of ToM and executive function and underwent MRI; parents rated children's psychosocial adjustment. Children with severe TBI demonstrated reduced right-hemisphere MN volume, and poorer ToM, vs children with OI. Ordinary least-squares path analysis indicated that right-hemisphere MN volume and ToM mediated the association between severe TBI and adjustment. Parallel analyses substituting the central executive network and executive function were not significant, suggesting some model specificity. Children at greatest risk of poor adjustment after TBI could be identified based in part on neuroimaging of social brain networks and assessment of social cognition and thereby more effectively allocate limited intervention resources.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".