Co-morbid tobacco use disorder and depression: A re-evaluation of smoking cessation therapy in depressed smokers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To provide a critical evaluation of nicotine use disorder co-morbidity in persons with major depressive disorder (MDD) or its subsyndromal presentations. We focus on how a diagnosis of current or past MDD may shape access to smoking cessation therapy, and highlight the unique challenges that this group of smokers has to overcome to receive adequate treatment. METHODS: A literature search was performed using PubMed for studies published between January 1995 and March 2015 using the following keywords and combination of keywords (co-morbidity, co-occurrence, and dual-diagnosis) and (nicotine dependence, cigarette smoking, tobacco dependence, tobacco use disorder) and (depression, major depression, unipolar mood disorders) and (self-medication). A total of 93 articles were identified. Of these, 31 studies were included in the analysis. RESULTS: We found that: a) depressed smokers are motivated to quit; b) smoking cessation does not exacerbate symptoms of depression; c) depression does not have a negative impact on smoking cessation outcomes, and d) the self-medication hypothesis does not account for tobacco dependence and depression co-morbidity. DISCUSSION AND CONCLUSIONS: Our review of the relevant evidence suggests the importance and clinical significance of undertaking smoking cessation treatment for depressed smokers. SCIENTIFIC SIGNIFICANCE: Our findings support the need for increased attention to developing and implementing smoking cessation treatments for depressed smokers. SCIENTIFIC SIGNIFICANCE: Our findings support the need for increased attention to developing and implementing smoking cessation treatments for depressed smokers.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".