Trauma’s distinctive and combined effects on subsequent substance use, mental health, and neurocognitive functioning with the NCANDA sample
Bibliographic record
Abstract
PURPOSE: Traumatic brain injury (TBI) and potentially traumatic events (PTEs) contribute to increased substance use, mental health issues, and cognitive impairments. However, there's not enough research on how TBI and PTEs combined impact mental heath, substance use, and neurocognition. METHODS: This study leverages a subset of The National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) multi-site dataset with 551 adolescents to assess the combined and distinctive impacts of TBI, PTEs, and TBI+PTEs (prior to age 18) on substance use, mental health, and neurocognitive outcomes at age 18. RESULTS: TBI, PTEs, and TBI+PTEs predicted greater lifetime substance use and past-year alcohol and cannabis use. PTEs predicted greater internalizing symptoms, while TBI+PTEs predicted greater externalizing symptoms. Varying effects on neurocognitive outcomes included PTEs influencing attention accuracy and TBI+PTEs predicting faster speed in emotion tasks. PTEs predicted greater accuracy in abstraction-related tasks. Associations with working memory were not detected. CONCLUSION: This exploratory study contributes to the growing literature on the complex interplay between TBI, PTEs, and adolescent mental health, substance use, and neurocognition. The developmental implications of trauma via TBIs and/or PTEs during adolescence are considerable and worthy of further investigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".