Evidence map›Paper›PMID 42568921›Full record

ArticlePreventive medicine reports2026

Shaoying Ma, Zefeng Qiu, Gloria Hernandez, Shiqi Zhang, Veronica Thai, Eden Chaudhry, Shuning Jiang, Ce Shang

Abstract read
In one paragraph

Article in Preventive medicine reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shaoying MaCenter for Tobacco Research, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Zefeng QiuDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Gloria HernandezCenter for Tobacco Research, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Shiqi ZhangCenter for Tobacco Research, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Veronica ThaiDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Eden ChaudhryCenter for Tobacco Research, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Shuning JiangDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Ce ShangCenter for Tobacco Research, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.

Funding

The Ohio State University Tobacco Center of Regulatory Science (OSU-TCORS)U54CA287392 · NCI · OHIO STATE UNIVERSITY · PI AHMAD EL HELLANI · 2023 to 2026
$19.5M
Rutgers Center of Excellence in Rapid Surveillance of TobaccoU01CA278695 · NCI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI CRISTINE D DELNEVO, Ollie Ganz · 2023 to 2026
$14.0M
NCI NIH HHS U01 CA278695NCI NIH HHS U54 CA287392
6 · The paper itself

Abstract

Objective: Flavor is a key driver of e-cigarette appeal, particularly among youth. This study examines concept flavors in U.S. online e-cigarette marketing, which are ambiguous descriptors used by the industry to evoke sensory experiences while circumventing flavor restrictions. Methods: We scraped product-level data of e-liquids and disposables in 2021 and 2024, respectively, from six popular online retailers. Flavor information was extracted from source code, product descriptions, and images. Using an expanded e-cigarette flavor wheel, we linked concept descriptors on packages to 12 explicit flavor categories. We also identified concept flavors suggesting cooling agents (e.g., "ice"). Results: Among 5368 e-liquid images from 2021, 1091 (20 %) featured concept flavors; among 3917 disposable images from 2024, 1256 (32 %) featured concept flavors. Across both years, we identified 754 unique concept flavor terms without ice mentions and 136 with ice mentions. Fruity and sweet flavors had the highest number of concept descriptors without ice mentions, whereas fruity, sweet, and menthol flavors had the highest numbers of concept descriptors with ice mentions. Conclusions: Concept flavor labeling is common in U.S. e-cigarette products. Our database facilitates rapid surveillance of e-cigarette products by the Food and Drug Administration and enables timely policy actions to regulate non-characterizing flavor labeling.

Indexed as

Concept flavorCoolingDisposable e-cigaretteE-cigaretteE-liquidFlavorLabelingOnline marketOnline storeWeb scraping

Identifiers

PMID42568921
PMCPMC13448710

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.