Personalized dosing of nicotine replacement therapy versus standard dosing for the treatment of individuals with tobacco dependence: study protocol for a randomized placebo-controlled trial
Bibliographic record
Abstract
BACKGROUND: Medications for smoking cessation are currently only effective in helping a minority of smokers quit. Drug development is slow and expensive; as such, there is much interest in optimizing the effectiveness of existing treatments and medications. Current standard doses of nicotine replacement therapy are not effective for many smokers, and in many cases, the amount of nicotine provided is much less than when a smoker is smoking their usual number of cigarettes. The proposed study will test if titrating the dose of the nicotine patch (up to 84 mg) will improve quitting success compared to those receiving a 21-mg nicotine patch with increasing doses of placebo patch. METHODS: This is a multicenter, pragmatic, two-arm, placebo-controlled, block randomized controlled trial. We will recruit participants who smoke at least 10 cigarettes daily and are interested in making a quit attempt. After 2 weeks of usual treatment with a 21-mg patch, participants who fail to quit smoking (target n = 400) will be randomized to receive escalating doses of a nicotine patch vs matching placebo patches for an additional 10 weeks or up to a maximum dose of 84 mg per day. Those who stop smoking during the first 2 weeks of usual treatment will continue with 21 mg patch treatment for 10 weeks and will form an additional comparison arm. In addition to the medication, participants will receive brief behavioral counseling at each study visit. The primary outcome will be biochemically confirmed continuous abstinence from smoking during the last 4 weeks of treatment (weeks 9 to 12). DISCUSSION: Research evidence supporting the effectiveness of personalized doses of nicotine replacement therapy could change current practice in a variety of healthcare settings. Given the evidence that quitting smoking at any age diminishes the risk of tobacco-related morbidity and mortality, even small increases in absolute quit rates can have a substantial population-level impact on reducing smoking-related disease, mortality rates, and associated healthcare costs. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03000387 . Registered on 22 December 2016.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".