Effects of Pediatric Traumatic Brain Injury on Verbal IQ: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objectives: To examine the effects of pediatric traumatic brain injury (TBI) on verbal IQ by severity and over time. Methods: A systematic review and subsequent meta-analysis of verbal IQ by TBI severity were conducted using a random effects model. Subgroup analysis included two epochs of time (e.g., <12 months postinjury and ≥12 months postinjury). Results: Nineteen articles met inclusion criteria after an extensive literature search in MEDLINE, PsycInfo, Embase, and CINAHL. Meta-analysis revealed negative effects of injury across severities for verbal IQ and at both time epochs except for mild TBI < 12 months postinjury. Statistical heterogeneity (i.e., between-study variability) stemmed from studies with inconsistent classification of mild TBI, small sample sizes, and in studies of mixed TBI severities, although not significant. Risk of bias on estimated effects was generally low (k = 15) except for studies with confounding bias (e.g., lack of group matching by socio-demographics; k = 2) and measurement bias (e.g., outdated measure at time of original study, translated measure; k = 2). Conclusions: Children with TBI demonstrate long-term impairment in verbal IQ, regardless of severity. Future studies are encouraged to include scores from subtests within verbal IQ (e.g., vocabulary, similarities, comprehension) in addition to functional language measures (e.g., narrative discourse, reading comprehension, verbal reasoning) to elucidate higher-level language difficulties experienced in this 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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".