More to the story than executive function: Effortful control soon after injury predicts long-term functional and social outcomes following pediatric traumatic brain injury in young children
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of early traumatic brain injury (TBI) on effortful control (EC) over time and the relationship of EC and executive functioning (EF) to long-term functional and social outcomes. METHOD: = 206, ages 3-7) with moderate-to-severe TBI or orthopedic injuries (OIs) rated EC using the Child Behavior Questionnaire at 1 (pre-injury), 6, 12, and 18 months post-injury. Child functioning and social competence were assessed at 7 years post-injury. Mixed models examined the effects of injury, time since injury, and their interaction on EC. General linear models examined the associations of pre-injury EC and EC at 18 months with long-term functional and social outcomes. Models controlled for EF to assess the unique contribution of EC to outcomes. RESULTS: Children with severe TBI had significantly lower EC than both the OI and moderate TBI groups at each post-injury time point. Both pre-injury and 18-month EC were associated with long-term outcomes. Among those with low EC at baseline, children with moderate and severe TBI had more functional impairment than those with OI; however, no group differences were noted at high levels of EC. EC had main effects on parent-reported social competence that did not vary by injury type. CONCLUSIONS: Findings suggest that EC is sensitive to TBI effects and is a unique predictor of functional outcomes, independent of EF. High EC could serve as a protective factor, and as such measures of EC could be used to identify children for more intensive intervention.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".