Depressive Symptoms and Alcohol Consumption among Nonalcoholic Depression Patients Treated with Desipramine
Bibliographic record
Abstract
OBJECTIVE: There are few data addressing the effect of alcohol consumption on response to antidepressants among nonalcoholics with depression. Similarly, the effect of antidepressant treatment on alcohol consumption in this group is not yet understood. This study focuses on changes in depressive symptoms and alcohol consumption in response to treatment with desipramine. METHOD: Twenty-seven nonalcoholic outpatients with major depression (as determined by the Schedule for Affective Disorders and Schizophrenia-Lifetime Version) completed measures of depression (that is, the 17-item Hamilton Depression Rating Scale and the Beck Depression Inventory) and alcohol consumption at intake and after 5 weeks of open treatment with desipramine. Subjects were characterized as minimal or mild-to-moderate drinkers. RESULTS: There was no significant difference between the groups with respect to effectiveness of antidepressant treatment. Analysis for repeated measures demonstrated that alcohol consumption with desipramine was significantly lower after treatment than at intake (F = 4.8, df 23:2, P < 0.01). Further, carbohydrate consumption was also significantly lower after treatment than at intake (F = 4.4, df 23:2, P < 0.05). CONCLUSIONS: Desipramine treatment appeared to result in decreases in alcohol consumption in nonalcoholic patients with depression. Further research is needed to elucidate the effect of alcohol consumption on the course and outcome of major depressive illness among nonalcoholics as well as the effect of antidepressant medication on alcohol consumption in this population.
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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".