ArticleHealthcare (Basel, Switzerland)2023
A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Predictors of vaping relapse based on e-cigarette use measures: A secondary analysis of longitudinal data from the 'Stop Vaping Challenge' cessation application.Tobacco induced diseases · 2026Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table.International journal of environmental research and public health · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Since 2016, there has been a substantial rise in e-cigarette (vaping) dependence among young people. In this prospective cohort study, we aimed to identify the different predictors of vaping dependence over 3 months among adolescents who were baseline daily and non-daily vapers. We recruited ever-vaping Canadian residents aged 16-25 years on social media platforms and asked them to complete a baseline survey in November 2020. A validated vaping dependence score (0-23) summing up their responses to nine questions was calculated at the 3-month follow-up survey. Separate lasso regression models were developed to identify predictors of higher 3-month vaping dependence score among baseline daily and non-daily vapers. Of the 1172 participants, 643 (54.9%) were daily vapers with a mean age of 19.6 ± 2.6 years and 76.4% (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.