The clinical utility of the Children’s Communication Checklist-2 in children with early childhood traumatic brain injury
Bibliographic record
Abstract
Objective Pediatric traumatic brain injury (TBI) is associated with long-term cognitive and behavioral deficits. Social communication impairments are common and impact functional outcomes, such as social engagement and academic performance. There are many barriers to identifying social communication deficits following TBI, including the absence of a standardized parent-reported communication measure for use in this population. The Children’s Communication Checklist—Second Edition (CCC-2) has demonstrated utility in identifying communication deficits in diagnoses other than TBI. This study investigated the clinical utility of the CCC-2’s social communication scales in children with TBI. Method: 203 children who sustained TBI or orthopedic injuries between the ages of 36 and 83 months were recruited as part of a larger, longitudinal study. We analyzed social communication subscale scores from the CCC-2 an average of 3.5 years postinjury. We used binary logistic regression analyses to examine the measure’s accuracy in classifying children with and without social communication deficits on other measures of pragmatic language and social competence. Correlation analyses and linear mixed models were used to examine the construct validity of the CCC-2. Results: The CCC-2 was able to accurately classify those with and without pragmatic language impairments on the Comprehensive Assessment of Spoken Language 92% of the time (sensitivity = 55%) and 96% of the time on the Home and Community Social Behavior scale (sensitivity = 72%). The CCC-2 demonstrated strong correlations with and predictive validity for measures of social communication and competence. Conclusions: The findings offer support for the clinical utility of the CCC-2 in the pediatric TBI population.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".