ArticleDecision support systems2016
TOY SAFETY SURVEILLANCE FROM ONLINE REVIEWS.
Article in Decision support systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
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.
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Who cites it
6 citing papers in PubMed.
- Safety Concerns in Mobility-Assistive Products for Older Adults: Content Analysis of Online Reviews.Journal of medical Internet research · 2023Article
- Characterization of More Than a Third of a Million Toy-Related Fractures.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2022Article
- Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers.F1000Research · 2022Article
- Assessment of the opportunities for increasing the availability of EU data on consumer product-related injuries.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2021Article
- The Public Health Challenge of Consumer Non-Compliance to Toy Product Recalls and Proposed Solutions.International journal of environmental research and public health · 2018Article
- Traumatic Cataract in Children in Eastern China: Shanghai Pediatric Cataract Study.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children's toys. We develop a danger word list, also known as a 'smoke word' list, from injury and recall text narratives. We then use the smoke word lists to score over one million Amazon reviews, with the top scores denoting potential safety concerns. We compare the smoke word list to conventional sentiment analysis techniques, in terms of both word overlap and effectiveness. We find that smoke word lists are highly distinct from conventional sentiment dictionaries and provide a statistically significant method for identifying safety concerns in children's toy reviews. Our findings indicate that text-mining is, in fact, an effective method for the surveillance of safety concerns in children's toys and could be a gateway to effective prevention of toy-product-related injuries.
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What OpenQuestion holds
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.