Recovery trajectories of IQ after pediatric TBI: A latent class growth modeling analysis
Bibliographic record
Abstract
OBJECTIVE: To identify latent trajectories of IQ over time after pediatric traumatic brain injury (TBI) and examine the predictive value of risk factors within and across recovery trajectories. METHOD: 206 children ages 3-7 years at injury were included: 87 TBI (23 severe, 21 moderate, 43 complicated mild) and 119 orthopedic injury (OI). We administered intelligence tests shortly after injury (1½ months), 12 months, and 6.8 years postinjury. Latent class growth modeling was used to identify latent subgroups. Separate models examined verbal and nonverbal IQ recovery trajectories following TBI versus OI. Variables included: age at injury, sex, race, socioeconomic status, injury severity, quality of the home environment, family functioning, and parenting style. RESULTS: = 3). Although all models resulted in 3 latent classes (below average, average, and aboveaverage performance); trajectory shapes, contributors to class membership, and performance within each class varied by injury group and IQ domain. TBI severity was associated with class membership for nonverbal IQ, with less severe injuries associated with higher IQ scores; however, TBI severity did not influence verbal IQ class membership. Parenting style had a more prominent effect on verbal and nonverbal IQ within the TBI than OI trajectories. CONCLUSIONS: Findings suggest TBI severity is related to recovery trajectories for nonverbal but not verbal IQ and parenting style has stronger effects on recovery in TBI than OI. Results highlight the importance of parental factors on long-term recovery after TBI.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".