ArticlePloS one2016
Online Discussion on #KidneyStones: A Longitudinal Assessment of Activity, Users and Content.
Article in PloS one, 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
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
6 citing papers in PubMed.
- The evolution of social media in nephrology education: A mini-review.Frontiers in nephrology · 2023Review
- The good in social media.BJUI compass · 2022Article
- Promoting Research, Awareness, and Discussion on AI in Medicine Using #MedTwitterAI: A Longitudinal Twitter Hashtag Analysis.Frontiers in public health · 2022Article
- A comprehensive analysis of #Enuresis conversation on Twitter.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2020Article
- A comparison of information sharing behaviours across 379 health conditions on Twitter.International journal of public health · 2019Article
- The activity and discussion points of #Circumcision through Twitter; a microblogging platform.International journal of impotence research · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTwitter is a popular microblogging platform for the rapid dissemination of information and reciprocal exchange in the urological field. We aimed to assess the activity, users and content of the online discussion, #KidneyStones, on Twitter.
methodsWe investigated the Symplur Signals analytics tool for Twitter data distributed via the #KidneyStones hashtag over a one year period. Activity analysis reflected overall activity and tweet enhancements. We assessed users' geolocations and performed an influencer analysis. Content analysis included the most frequently used words, tweet sentiment and shares for top tweets.
results3,426 users generated over 10,333 tweets, which were frequently accompanied by links (49%), mentions (30%) and photos (13%). Users came from 106 countries across the globe and were most frequently from North America (63%) and Europe (16%). Individual and organisational healthcare professionals made up 56% of the influencers of the Twitter discussion on #KidneyStones. Besides the words 'kidney' (used 4,045 times) and 'stones' (3,335), 'pain' (1,233), 'urine' (1,158), and 'risk' (1,023) were the most frequently used words. 56% of tweets had a positive sentiment. The median (range) number of shares was 85 (62-587) for the top 10 links, 45.5 (17-94) for the top 10 photos, and 44 (22-95) for the top 10 retweets.
conclusionThe rapidly growing Twitter discussion on #KidneyStones engaged multiple stakeholders in the healthcare sector on a global scale and reached both professionals and laypeople. When used effectively and responsibly, the Twitter platform could improve prevention and medical care of kidney stone patients.
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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.