Evidence map›Paper›PMID 37168528›Full record

ArticleFrontiers in digital health2023

Retrospective content analysis of consumer product reviews related to chronic pain.

Jungwei W Fan, Wanjing Wang, Ming Huang, Hongfang Liu, W Michael Hooten

Abstract read
In one paragraph

Article in Frontiers in digital health, 2023. 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

5 authors.

Jungwei W FanDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
Wanjing WangDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.
Ming HuangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
Hongfang LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
W Michael HootenDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic pain (CP) lasts for more than 3 months, causing prolonged physical and mental burdens to patients. According to the US Centers for Disease Control and Prevention, CP contributes to more than 500 billion US dollars yearly in direct medical cost plus the associated productivity loss. CP is complex in etiology and can occur anywhere in the body, making it difficult to treat and manage. There is a pressing need for research to better summarize the common health issues faced by consumers living with CP and their experience in accessing over-the-counter analgesics or therapeutic devices. Modern online shopping platforms offer a broad array of opportunities for the secondary use of consumer-generated data in CP research. In this study, we performed an exploratory data mining study that analyzed CP-related Amazon product reviews. Our descriptive analyses characterized the review language, the reviewed products, the representative topics, and the network of comorbidities mentioned in the reviews. The results indicated that most of the reviews were concise yet rich in terms of representing the various health issues faced by people with CP. Despite the noise in the online reviews, we see potential in leveraging the data to capture certain consumer-reported outcomes or to identify shortcomings of the available products.

Indexed as

chronic painconsumer health informaticsdata miningonline product reviewsretrospective study

Identifiers

PMID37168528
PMCPMC10165495

What OpenQuestion holds

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Registered trials

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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.