Is the Relationship Between Major Depressive Disorder and Self-Reported Alcohol Use Disorder an Artificial One?
Bibliographic record
Abstract
AIMS: Many studies have suggested a close relationship between alcohol use disorder (AUD) and major depressive disorder (MDD). This study aimed to test whether the relationship between self-reported AUD and MDD was artificially strengthened by the diagnosis of MDD. This association was tested comparing relationships between alcohol use and AUD for depressive people and non-depressive people. METHODS: As part of the Cohort Study on Substance Use Risk Factors, 4352 male Swiss alcohol users in their early twenties answered questions concerning their alcohol use, AUD and MDD at two time points. Generalized linear models for cross-sectional and longitudinal associations were calculated. RESULTS: For cross-sectional associations, depressive participants reported a higher number of AUD symptoms (β = 0.743, P < 0.001) than non-depressive participants. Moreover, there was an interaction (β = -0.204, P = 0.001): the relationship between alcohol use and AUD was weaker for depressive participants rather than non-depressive participants. For longitudinal associations, there were almost no significant relationships between MDD at baseline and AUD at follow-up, but the interaction was still significant (β = -0.249, P < 0.001). CONCLUSION: MDD thus appeared to be a confounding variable in the relationship between alcohol use and AUD, and self-reported measures of AUD seemed to be overestimated by depressive people. This result brings into question the accuracy of self-reported measures of substance use disorders. Furthermore, it adds to the emerging debate about the usefulness of substance use disorder as a concept, when heavy substance use itself appears to be a sensitive and reliable indicator.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".