IQ After Pediatric Concussion
Bibliographic record
Abstract
OBJECTIVES: This study investigated IQ scores in pediatric concussion (ie, mild traumatic brain injury) versus orthopedic injury. METHODS: Children (N = 866; aged 8-16.99 years) were recruited for 2 prospective cohort studies from emergency departments at children's hospitals (2 sites in the United States and 5 in Canada) ≤48 hours after sustaining a concussion or orthopedic injury. They completed IQ and performance validity testing postacutely (3-18 days postinjury; United States) or 3 months postinjury (Canada). Group differences in IQ scores were examined using 3 complementary statistical approaches (linear modeling, Bayesian, and multigroup factor analysis) in children performing above cutoffs on validity testing. RESULTS: Linear models showed small group differences in full-scale IQ (d [95% confidence interval] = 0.13 [0.00-0.26]) and matrix reasoning (0.16 [0.03-0.30]), but not in vocabulary scores. IQ scores were not related to previous concussion, acute clinical features, injury mechanism, a validated clinical risk score, pre- or postinjury symptom ratings, litigation, or symptomatic status at 1 month postinjury. Bayesian models provided moderate to very strong evidence against group differences in IQ scores (Bayes factor 0.02-0.23). Multigroup factor analysis further demonstrated strict measurement invariance, indicating group equivalence in factor structure of the IQ test and latent variable means. CONCLUSIONS: Across multisite, prospective study cohorts, 3 complementary statistical models provided no evidence of clinically meaningful differences in IQ scores after pediatric concussion. Instead, overall results provided strong evidence against reduced intelligence in the first few weeks to months after pediatric concussion.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".