ArticlePloS one2020
A simple predictive model for estimating relative e-cigarette toxic carbonyl levels.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Carbonyl Emissions and Heating Temperatures across 75 Nominally Identical Electronic Nicotine Delivery System Products: Do Manufacturing Variations Drive Pulmonary Toxicant Exposure?Chemical research in toxicology · 2023Article
- A First-Tier Framework for Assessing Toxicological Risk from Vaporized Cannabis Concentrates.Toxics · 2022Article
- Chemical characterisation of the vapour emitted by an e-cigarette using a ceramic wick-based technology.Scientific reports · 2022Article
- Vaping and Lung Inflammation and Injury.Annual review of physiology · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
E-cigarette devices are wide ranging, leading to significant differences in levels of toxic carbonyls in their respective aerosols. Power can be a useful method in predicting relative toxin concentrations within the same device, but does not correlate well to inter-device levels. Herein, we have developed a simple mathematical model utilizing parameters of an e-cigarette's coil and wick in order to predict relative levels of e-liquid solvent degradation. Model 1, which is coil length/(wick surface area*wraps), performed in the moderate-to-substantial range as a predictive tool (R2 = 0.69). Twelve devices, spanning a range of coil and wick styles, were analyzed. Model 1 was evaluated against twelve alternative models and displayed the best predictability. Relationships that included power settings displayed weak predictability, validating that power levels cannot be reliably compared between devices due to differing wicking and coil components and heat transfer efficiencies.
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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.