Atomoxetine treatment of adults with ADHD and comorbid alcohol abuse disorder
Bibliographic record
Abstract
Objective: Adults with ADHD have higher rates of alcohol use disorders (AUD) than controls. This study tested the hypothesis that atomoxetine (Atmx)is superior to placebo in the treatment of ADHD and alcohol use in recently abstinent adult subjects with ADHD and comorbid AUD. Methods: Subjects were adults who met DSM-IV-TR criteria for both ADHD and AUD and were abstinent from alcohol 4–30d before study entry. Participants received Atmx (25–100mg/d)or placebo for 12 wks. Standard measurements for ADHD and AUD were used. Time to relapse of alcohol abuse was analyzed using a 2-sided log-rank test based on Kaplan-Meier estimates. Cumulative heavy drinking events over time were evaluated post hoc with a recurrent event analysis. Results: A total of 147 subjects received Atmx (n=72) or placebo (n=75),80 completed the 12 wk double-blind period (n=32 and 48). ADHD symptoms were improved in the Atmx cohort vs. placebo (P=.007). Time to relapse showed no significant differences between treatment arms; however recurrent event analysis showed that Atmx reduced the cumulative number of heavy drinking episodes by approx. 26% compared to placebo (P=.023). There were no serious adverse events or specific drug-drug reactions related to current alcohol use. Conclusions: This controlled study of adult ADHD subjects in very recent remission from AUD demonstrates robust effects in improving ADHD and suggests an effect of atomoxetine reducing cumulative heavy drinking events over time.
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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) | 0.001 | 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) | 0.002 | 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".