Predictors of Very-Long-Term Sociocognitive Function after Pediatric Traumatic Brain Injury: Evidence for the Vulnerability of the Immature “Social Brain”
Bibliographic record
Abstract
Emotion perception (EP) forms an integral part of social communication and is critical to attain developmentally appropriate goals. This skill, which emerges relatively early in development, is driven by increasing connectivity among regions of a distributed sociocognitive neural network and may be vulnerable to disruption from early-childhood traumatic brain injury (TBI). The present study aimed to evaluate the very-long-term effect of childhood TBI on EP, as well as examine the contribution of injury- and non-injury-related risk and resilience factors to variability in sociocognitive outcomes. Thirty-four young adult survivors of early-childhood TBI (mean [M], 20.62 years; M time since injury, 16.55 years) and 16 typically developing controls matched for age, gender, and socioeconomic status were assessed using tasks that required recognition and interpretation of facial and prosodic emotional cues. Survivors of severe childhood TBI were found to have significantly poorer emotion perception than controls and young adults with mild-to-moderate injuries. Further, poorer emotion perception was associated with reduced volume of the posterior corpus callosum, presence of frontal pathology, lower SES, and a less-intimate family environment. Our findings lend support to the vulnerability of the immature "social brain" network to early disruption and underscore the need for context-sensitive rehabilitation that optimizes early family environments to enhance recovery of EP skills after childhood TBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".