Original Contribution Duration of Nicotine Replacement Therapy Use and Smoking Cessation: A Population-Based Longitudinal Study
Bibliographic record
Abstract
In the present study, we examined the association between duration of nicotine replacement therapy (NRT) use and smoking cessation using data from the Ontario Tobacco Survey longitudinal study (3 waves of data collected between July 2005 and December 2009). We used logistic regression with generalized estimating equations to ex-amine the association between NRT use (any use and <4 weeks, 4.0–7.9 weeks, 8.0–11.9 weeks, and ≥12 weeks of use compared with nonuse) and quitting smoking (≥1 month). Using NRTwas not associated with quitting when use duration was not taken into account (adjusted odds ratio (OR) = 1.08, 95 % confidence interval (CI): 0.86, 1.35). Compared with abstaining from NRT when attempting to quit smoking, using NRT for less than 4 weeks was asso-ciated with a lower likelihood of quitting (adjusted OR = 0.51, 95 % CI: 0.38, 0.67); however, using NRT for 4 weeks or longer was associated with a higher likelihood of cessation (for 4.0–7.9 weeks of NRT use, adjusted OR = 2.26, 95 % CI: 1.58, 3.22; for 8.0–11.9 weeks of NRT use, adjusted OR = 3.84, 95 % CI: 2.24, 6.58; and for ≥12 weeks of NRT use, adjusted OR = 2.80, 95 % CI: 1.70, 4.61). Thus, use of NRT for less than 4 weeks was associated with reduced likelihood of cessation, whereas NRTuse for longer periods of timewas associated with a higher likelihood of cessation. logistic models; longitudinal studies; nicotine replacement therapy; smoking cessation Abbreviations: NRT, nicotine replacement therapy; OR, odds ratio. Nicotine replacement therapy (NRT), including nicotine
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".