A Systematic Review of Combination Therapies for Smoking Cessation
Bibliographic record
Abstract
Introduction: The use of pharmacological and behavioural therapies has been shown to help smokers quit. However, the efficacy of combining smoking cessation therapies remains poorly understood. We conducteda systematic review of randomized controlled trials (RCTs) with factorial designs to assess the efficacy of combination smoking cessation therapies.Methods: We performed a systematic search of the Cochrane Library, EMBASE, PsycINFO, and PubMed databases for RCTs of combination therapies for smoking cessation. We included RCTs with factorial designs,reporting biochemically validated point prevalence or continuous abstinence outcomes at 6 or 12 months.Combination therapies were either two pharmacotherapies or apharmacotherapy with behavioural therapy.Pharmacotherapies included nicotine replacement therapies (NRTs), bupropion, and varenicline. Behavioural therapies included counselling and minimal intervention.Results: A total of 11 RCTs met our inclusion criteria: 4 combinations of pharmacotherapies and 7 combinations of a pharmacotherapy with behavioural intervention. Combinations were two NRTs (2 RCTs), bupropion with NRT (3 RCTs), bupropion with behavioural intervention (4 RCTs), and NRT with behavioural intervention (3 RCTs). No identified trials combined varenicline with other included pharmacotherapies. Combining pharmacotherapies did not increase smoking abstinence at 6 or 12 months, compared with pharmacologicalmonotherapies. Evidence suggests a modest yet inconsistent benefit from combining pharmacotherapy withbehavioural therapy.Conclusion: Evidence from RCTs with factorial designs does not conclusively show combination smoking cessation therapies to be superior to monotherapies. Pharmacotherapies could be prescribed without behavioural therapy, with minimal loss of treatment efficacy.Key words: Smoking cessation, combination therapy, systematic review
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".