Systematic Biases in Cross-sectional Community Studies may Underestimate the Effectiveness of Stop-Smoking Medications
Bibliographic record
Abstract
INTRODUCTION: Randomized, controlled trials typically indicate stop-smoking medications (SSMs: e.g., Varenicline, Bupropion, and over-the-counter nicotine replacement therapies) to be effective, whereas cross-sectional community-based studies have found them to be less effective, ineffective, or even associated with higher risk of relapse. Consequently, some critics have suggested SSMs have no useful applications in "real-world" settings. This discrepancy may, however, be due to systematic biases affecting cross-sectional survey outcomes. Namely, failed quit attempts where SSMs were used may be better recalled than failed unassisted attempts. Moreover, smokers who choose to quit using SSMs may be more addicted and thus less likely to succeed. Either of these factors would lead to an over-representation of failed quit attempts among SSM users in cross-sectional surveys even if there were real benefits. METHODS: We report on data from the International Tobacco Control 4-country cohort study to examine the relationship between SSM use, level of nicotine addiction, and the reported date since the start of participants' (N = 1,101) most recent quit attempt. RESULTS: The last quit attempt was reported to have begun longer ago among participants who used SSMs than those who did not. Scores on the Heaviness of Smoking Index, measuring addiction severity, were also higher among SSM users, with no interactions. CONCLUSION: Better recall of quit attempts and stronger addiction to nicotine are two characteristics found more often among smokers using SSMs compared with self-quitters, which could potentially bias the assessed effects of SSMs on cessation outcomes in cross-sectional surveys.
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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 | 0.342 | 0.540 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".