Effectiveness of stop‐smoking medications: findings from the <scp>I</scp>nternational <scp>T</scp>obacco <scp>C</scp>ontrol (<scp>ITC</scp>) <scp>F</scp>our <scp>C</scp>ountry <scp>S</scp>urvey
Bibliographic record
Abstract
AIM: To evaluate the population effectiveness of stop-smoking medications while accounting for potential recall bias by controlling for quit attempt recency. DESIGN: Prospective cohort survey. SETTING: United Kingdom, Canada, Australia and the United States. PARTICIPANTS: A total of 7436 adult smokers (18+ years) selected via random digit dialling and interviewed as part of the International Tobacco Control Four Country Survey (ITC-4) between 2002 and 2009. Primary analyses utilized the subset of respondents who participated in 2006 or later (n = 2550). MEASUREMENTS: Continuous abstinence from smoking for 1 month/6 months. FINDINGS: Among participants who recalled making a quit attempt within 1 month of interview, those who reported using varenicline, bupropion or nicotine patch were more likely to maintain 6-month continuous abstinence from smoking compared to those who attempted to quit without medication [adjusted odds ratio (OR) 5.84, 95% confidence interval (CI) (2.12-16.12), 3.94 (0.87-17.80), 4.09 (1.72-9.74), respectively]; there were no clear effects for oral NRT use. Those who did not use any medication when attempting to quit tended to be younger, to be racial/ethnic minorities, to have lower incomes and to believe that medications do not make quitting easier. CONCLUSIONS: Consistent with evidence from randomized controlled trials, smokers in the United Kingdom, Canada, Australia and the United States are more likely to succeed in quit attempts if they use varenicline, bupropion or nicotine patch. Previous population studies that failed to find an effect failed to control adequately for important sources of bias.
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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 | 0.005 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".