Impulsivity Impacts Suicidality in Patients with Alcohol Use Disorder with and without Comorbid Depression
Bibliographic record
Abstract
Abstract Objective: The independent rôle of impulsivity in alcoholism patient’s suicidality is less elucidated. In this study, we intended to investigate how impulsivity and depression contribute to suicidality in patients with alcohol use disorder (AUD). Methods: We recruited 27 adult patients with AUD and major depressive disorder (AUD with MDD) and 33 with AUD only (AUD without MDD). We assessed suicidality, alcohol use severity, depression severity, impulsivity, and other psychiatric comorbidities. Suicidality was quantified for the frequency of previous history of suicide attempts and for current suicide intent/tendency. Impulsivity was measured using the Barratt Impulsiveness Scale (BIS). We addressed how impulsivity contributed to both suicidality indices in multiple ordinal regressions. Results: Patients with AUD with MDD versus those with AUD without MDD showed significantly higher suicidality ( p < 0.001), significantly more severe alcohol ( p < 0.01), significantly more polysubstance use ( p < 0.05), significantly more anxiety comorbidities ( p < 0.05), and significantly higher BIS scores ( p < 0.001). In a better-fitting final model using regression with stepwise elimination, the BIS total score was independently and significantly associated with current suicide tendency ( p < 0.05) and frequency of previous suicide attempts ( p < 0.01). In contrast, the Hamilton Depression Rating Scale-17 score was significantly associated only with current suicidal tendencies ( p < 0.01), but not with the frequency of previous attempts. Conclusion: Depressed patients relative to nondepressed individuals with AUD showed higher suicidality, Barratt impulsivity, and severity of alcohol use. Across groups, BIS impulsivity but not the severity of depression was found to predict suicidality. We suggest that suicide prevention efforts may include assessment of impulsivity in AUD patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".