The association between symptom burden and processing speed and executive functioning at 4 and 12 weeks following pediatric concussion
Bibliographic record
Abstract
Abstract Objectives: Symptoms and cognition are both utilized as indicators of recovery following pediatric concussion, yet their interrelationship is not well understood. This study aimed to investigate: 1) the association of post-concussion symptom burden and cognitive outcomes (processing speed and executive functioning [EF]) at 4 and 12 weeks after pediatric concussion, and 2) the moderating effect of sex on this association. Methods: This prospective, multicenter cohort study included participants aged 5.00–17.99 years with acute concussion presenting to four Emergency Departments of the Pediatric Emergency Research Canada network. Five processing speed and EF tasks and the Post-Concussion Symptom Inventory (PCSI; symptom burden, defined as the difference between post-injury and retrospective [pre-injury] scores) were administered at 4 and 12 weeks post-concussion. Generalized least squares models were conducted with task performances as dependent variables and PCSI and PCSI*sex interaction as the main predictors, with important pre-injury demographic and injury characteristics as covariates. Results: 311 children (65.0% males; median age = 11.92 [IQR = 9.14–14.21 years]) were included in the analysis. After adjusting for covariates, higher symptom burden was associated with lower Backward Digit Span (χ2= 9.85,p= .043) and Verbal Fluency scores (χ2= 10.48,p= .033) across time points; these associations were not moderated by sex,ps≥ .20. Symptom burden was not associated with performance on the Coding, Continuous Performance Test, and Color-Word Interference scores,ps≥ .17. Conclusions: Higher symptom burden is associated with lower working memory and cognitive flexibility following pediatric concussion, yet these associations were not moderated by sex. Findings may inform concussion management by emphasizing the importance of multifaceted assessments of EF.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".