Attentional–inhibitory control and social–behavioral regulation after childhood closed head injury: Do biological, developmental, and recovery variables predict outcome?
Bibliographic record
Abstract
Attentional-inhibitory control and social-behavioral regulation are two outcome domains commonly impaired after childhood closed head injury (CHI). We compared neuropsychological tests of attentional-inhibitory control (vigilance, selective attention, response modulation) and social discourse and intentionality (inferencing, figurative language, and speech acts) with parent ratings of attention and behavioral regulation in relation to four injury-related variables: age at CHI, time since CHI, CHI injury severity, and frontal lobe injury moderated by CHI severity. Participants were 105 school-aged children in the chronic stage of CHI, divided into mild, moderate, and severe injury severity groups, and further subdivided according to frontal lobe injury. Outcome indices were imperfectly correlated in the group as a whole, although several relations between neurocognitive tests and parent ratings were observed within CHI subgroups. Different domains of cognitive function had different predictors. For attentional-inhibitory control, age at injury and time since injury were most predictive of outcome; for social discourse, predictors were injury severity and frontal lobe injury moderated by injury severity. Variability in cognitive outcome after childhood CHI is not random, but appears related to age, time, and biological features of the injury.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".