Benefits of E-Cigarettes Among Heavy Smokers Undergoing a Lung Cancer Screening Program: Randomized Controlled Trial Protocol
Bibliographic record
Abstract
BACKGROUND: Smoking is a global public health problem. For this reason, experts have called smoking dependence a global epidemic. Over the past 5 years, sales of electronic cigarettes, or e-cigarettes, have been growing strongly in many countries. Yet there is only partial evidence that e-cigarettes are beneficial for smoking cessation. In particular, although it has been proven that nicotine replacement devices may help individuals stop smoking and tolerate withdrawal symptoms, e-cigarettes' power to increase the quitting success rate is still limited, ranging from 5% to 20% dependent on smokers' baseline conditions as shown by a recent Cochrane review. Consequently, it is urgent to know if e-cigarettes may have a higher success rate than other nicotine replacement methods and under what conditions. Furthermore, the effects of the therapeutic setting and the relationship between individual characteristics and the success rate have not been tested. This protocol is particularly innovative, because it aims to test the effectiveness of electronic devices in a screening program (the COSMOS II lung cancer prevention program at the European Institute of Oncology), where tobacco reduction is needed to lower individuals' lung cancer risks. OBJECTIVE: This protocol was designed with the primary aim of investigating the role of tobacco-free cigarettes in helping smokers improve lung health and either quit smoking or reduce their tobacco consumption. In particular, we aim to investigate the impact of a 3-month e-cigarettes program to reduce smoking-related respiratory symptoms (eg, dry cough, shortness of breath, mouth irritation, and phlegm) through reduced consumption of tobacco cigarettes. Furthermore, we evaluate the behavioral and psychological (eg, well-being, mood, and quality of life) effects of the treatment. METHODS: This is a prospective, randomized, placebo-controlled, double-blind, three-parallel group study. The study is organized as a nested randomized controlled study with 3 branches: a nicotine e-cigarettes group, a nicotine-free e-cigarettes group, and a control group. The study is nested in a screening program for early lung cancer detection in heavy smokers. RESULTS: The study is open and is still recruiting. CONCLUSIONS: Stopping or reducing tobacco consumption should be a main goal of any health organization. However, traditional antismoking programs are expensive and not always effective. Therefore, favoring a partial or complete shift to e-cigarettes in heavy smokers (eg, persons at high risk for a number of diseases) could be considered a moral imperative. However, before following this path, sound and reliable data on large samples and in a variety of contexts are required. TRIAL REGISTRATION: Clinicaltrials.gov NCT02422914; https://clinicaltrials.gov/ct2/show/NCT02422914 (Archived by WebCite at http://www.webcitation.org/6etwz1bPL).
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".