Efficacy of pharmacotherapies for short-term smoking abstinance: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Smoking cessation has important immediate health benefits. The comparative short-term effectiveness of smoking cessation interventions is not well known. We aimed to determine the relative effectiveness of nicotine replacement therapy (NRT), bupropion and varenicline at 4 weeks post-target quit date. METHODS: We searched 10 electronic medical databases (inception to October 2008). We selected randomized clinical trials [RCTs] evaluating interventions for our primary outcome of abstinence from smoking at at-least 4 weeks post-target quit date, with biochemical confirmation. We conducted random-effects odds ratio (OR) meta-analysis and meta-regression. We compared treatment effects across interventions using head-to-head trials and calculated indirect comparisons. RESULTS: We combined a total of 101 trials evaluating delivery of NRT versus inert controls at approximately 4 weeks post-target quit date (total n = 31,321). The pooled overall OR is OR 2.05 (95% Confidence Interval [CI], 1.89-2.23, P =< 0.0001). We pooled data from 31 bupropion trials contributing a total n of 11,118 participants and found a pooled OR of 2.25 (95% CI, 1.94-2.62, P =< 0.0001). We evaluated 9 varenicline trials compared to placebo. Our pooled estimate for cessation at 4 weeks post-target quit date found a pooled OR of 3.16 (95% CI, 2.55-3.91, P =< 0.0001). Two trials evaluated head to head comparisons of varenicline and bupropion and found a pooled estimate of OR 1.86 (95% CI, 1.49-2.33, P =< 0.0001 at 4 weeks post-target quit date. Indirect comparisons were: NRT and bupropion, OR, 1.09, 95% CI, 0.93-1.31, P = 0.28; varenicline and NRT, OR 1.56, 95% CI, 1.23-1.96, P = 0.0002; and, varenicline and bupropion, OR 1.40, 95% CI, 1.08-1.85, P = 0.01. CONCLUSION: Pharmacotherapeutic interventions are effective for increasing smoking abstinence rates in the short-term.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".