Real-World Effectiveness of Varenicline Versus Nicotine Replacement Therapy in Patients With and Without Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness and safety of varenicline with nicotine replacement therapy (NRT) among smokers with or without psychiatric disorders attending a storefront smoking cessation clinic in an urban addiction and mental health academic health science center. METHODS: A retrospective chart review was conducted to compare treatment outcomes, demographics, and clinical characteristics for adult smokers prescribed varenicline (n = 98) or NRT (n = 98) between 2007 and 2010. Subjects were matched 1:1 on age, sex, and year of initial assessment. RESULTS: End-of-treatment quit rates were almost twice as high among those prescribed varenicline (33.7%) versus NRT (18.4%) (RR = 1.83, 95% CI = 1.11-3.03, P = 0.02). After adjusting for several baseline and treatment characteristics, varenicline was still significantly more effective than NRT (ARR = 1.71, 95% CI = 1.05-2.79, P = 0.03). History of psychiatric disorder (excluding substance use disorders) and treatment duration were also independent predictors of end-of-treatment quit rates. Nausea was more commonly reported among those using varenicline (13.3% vs 3.1%, P = 0.009). No single neuropsychiatric adverse effect significantly differed between groups; however, overall reporting of any neuropsychiatric effect was somewhat higher in the varenicline group (31.6% vs 20.4%, P = 0.07). There was one incident of suicidal ideation in each medication group. CONCLUSIONS: Varenicline seems to be more effective than NRT and as safe in real-world settings among patients with and without a history of psychiatric disorder.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".