Neuroanatomical predictors of problematic alcohol consumption in adolescents: a systematic review of longitudinal studies
Bibliographic record
Abstract
AIMS: This study aimed to systematically review the literature on neuroanatomical predictors of future problematic drinking in adolescents. METHODS: Using PRISMA guidelines, a systematic review was conducted to evaluate neuroanatomical predictors of problematic alcohol consumption in adolescents. EMBASE, MEDLINE, and PsycINFO databases were searched from inception to 6 January 2023. Studies were included if they were original, had a prospective design, had a sample size of at least 12, had a follow-up period of at least 1 year, had at least one structural neuroimaging scan before 18 with no prior alcohol use, and had alcohol use as the primary outcome. Studies were excluded if they had animals only and were not in English. Risk of bias was conducted using the CASP tool. RESULTS: Out of 1412 studies identified, 19 studies met the criteria, consisting of 11 gray matter (n = 4040), 5 white matter (n = 319), and 3 assessing both (n = 3608). Neuroanatomical predictors of future problematic drinking in adolescents were reported to be distributed across various brain regions such as the orbitofrontal cortex and paralimbic regions. However, the findings were largely heterogeneous. CONCLUSIONS: This is the first systematic review to map out the existing literature on neuroanatomical predictors of problematic drinking in adolescents. Future research should focus on the aforementioned regions to determine their role in predicting future problematic drinking with more certainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".