Catecholamine levels and delay discounting forecast drug use among <scp>A</scp>frican <scp>A</scp>merican youths
Bibliographic record
Abstract
AIMS: To test hypotheses about the contributions of the catecholamines epinephrine and norepinephrine [which serve as biological markers of life stress through sympathetic nervous system (SNS) activation], delay discounting and their interaction to the prediction of drug use among young African American adults. DESIGN: A 1-year prospective study that involved assessment of SNS activity and collection of self-report data involving delay discounting and drug use. SETTING: Rural communities in the southeastern United States. PARTICIPANTS: A total of 456 African Americans who were 19 years of age at the beginning of the study. MEASUREMENTS: At age 19, participants provided overnight urine voids that were assayed for epinephrine and norepinephrine. Participants were also assessed for hyperbolic temporal discounting functions (k) and drug use. At age 20, the participants again reported their drug use. FINDINGS: Linear regression analyses revealed that (i) catecholamine levels at age 19 forecast increases in drug use [B = 0.087, P < 0.01, 95% confidence interval (CI) = 0.025, 0.148] and (ii) among young men, catecholamine levels interacted positively with delay discounting to forecast increases in drug use (simple slope = 0.113, P < 0.001, 95% CI = 0.074, 0.152). CONCLUSIONS: Higher urinary catecholamine concentrations in their adulthood predict higher levels of drug use a year later among young African American men in the United States who engage in high, but not low, levels of delay discounting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".