Risk profiles for heavy drinking in adolescence: differential effects of gender
Bibliographic record
Abstract
Abnormalities across different domains of neuropsychological functioning may constitute a risk factor for heavy drinking during adolescence and for developing alcohol use disorders later in life. However, the exact nature of such multi-domain risk profiles is unclear, and it is further unclear whether these risk profiles differ between genders. We combined longitudinal and cross-sectional analyses on the large IMAGEN sample (N ≈ 1000) to predict heavy drinking at age 19 from gray matter volume as well as from psychosocial data at age 14 and 19-for males and females separately. Heavy drinking was associated with reduced gray matter volume in 19-year-olds' bilateral ACC, MPFC, thalamus, middle, medial and superior OFC as well as left amygdala and anterior insula and right inferior OFC. Notably, this lower gray matter volume associated with heavy drinking was stronger in females than in males. In both genders, we observed that impulsivity and facets of novelty seeking at the age of 14 and 19, as well as hopelessness at the age of 14, are risk factors for heavy drinking at the age of 19. Stressful life events with internal (but not external) locus of control were associated with heavy drinking only at age 19. Personality and stress assessment in adolescents may help to better target counseling and prevention programs. This might reduce heavy drinking in adolescents and hence reduce the risk of early brain atrophy, especially in females. In turn, this could additionally reduce the risk of developing alcohol use disorders later in adulthood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".