Evidence map›Paper›PMID 42477636›Full record

ArticleBMC oral health2026

Information completeness, sentiment, and engagement of orthodontic patient experience content on Chinese social media: a cross-sectional study.

Zehua Jin, Jin'e Zhang, Zhijue Zhang, Xueyan Xiong, Fan Zhang

Abstract read
In one paragraph

Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zehua Jin *Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Jin'e Zhang *Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Zhijue Zhang *Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Xueyan Xiong *Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. dfkqxxy@163.com.
Fan Zhang *Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. zhangfan99@163.com.

Funding

Young Physician New Technology Incubation Fund of Shanghai East Hospital Grant No. 2025-DFQNNT-003Youth Scientific Research Cultivation Fund of Shanghai East Hospital Grant No. DFPY2025002
6 · The paper itself

Abstract

backgroundSocial media has become an important source of orthodontic information, yet patient experience content varies in completeness and may shape treatment expectations. This study evaluated information completeness, comment sentiment, and engagement patterns in orthodontic experience content on three major Chinese social media platforms.

methodsA cross-sectional sample of 180 eligible posts was collected from Bilibili, Douyin, and Xiaohongshu using two search terms, "orthodontic experience" and "braces diary." Information completeness was assessed using a modified 6-point Information Completeness Score (ICS). Comment sentiment and title sentiment were quantified using sentiment analysis scores (SAS). Engagement metrics, including likes, favorites, shares, and comments, were recorded. Platform differences and subgroup differences by appliance type, treatment population, video duration, and video age were analyzed.

resultsInter-rater reliability for total ICS was excellent (ICC (3,1) = 0.954). The automated SAS classifications showed strong agreement with the human consensus gold standard, with an overall accuracy of 91.6% (κ = 0.848). ICS differed significantly across platforms (H = 40.70, p < 0.001), with Bilibili showing the highest score (3.53 ± 1.46) and Xiaohongshu the lowest (1.78 ± 1.24). Treatment details and treatment duration were the most frequently mentioned dimensions, whereas clinician or institution credentials and cost were least frequently covered. Title sentiment showed a statistically significant but weak positive correlation with comment sentiment in the full sample (rho = 0.247, p < 0.001), with platform-specific variation. Engagement indicators were strongly intercorrelated (rho = 0.69-0.87), but ICS showed no meaningful correlation with engagement. Longer videos, clear aligner content, adult orthodontic content, and older videos had higher ICS, whereas SAS varied only slightly across groups.

conclusionsOrthodontic patient experience content on Chinese social media shows substantial variation in information completeness. High engagement does not necessarily indicate high information quality. Professional orthodontic content should provide balanced information on treatment benefits, risks, costs, duration, and professional sources to support informed patient decision-making.

Indexed as

OrthodonticsSocial MediaChinaCross-Sectional StudiesDigital MediaHumansPatient SatisfactionInformation qualityOrthodonticsPatient testimonialsSentiment analysisSocial media

Identifiers

PMID42477636
PMCPMC13663261

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.