Evidence map›Paper›PMID 32459647›Full record

ArticleJMIR mHealth and uHealth2020

Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study.

Zhi Chun Wang, Shi Ping Zhang, Pong Chi Yuen, Kam Wa Chan, Yi Yi Chan, Chun Hoi Cheung, Chi Ho Chow, Ka Kit Chua, Jun Hu, Zhichao Hu and 14 more

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.5field-weighted citation impact, top 6% of its field
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

17 citing papers in PubMed, 27 citations in OpenAlex.

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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

24 authors at 7 institutions in 2 countries.

Zhi Chun WangSchool of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-9045-6831
Shi Ping ZhangSchool of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-6413-5778
Pong Chi YuenSchool of Computing Science, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-9343-2202
Kam Wa Chan *Department of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-3175-1574
Yi Yi Chan *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-9226-0576
Chun Hoi Cheung *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0003-2936-2921
Chi Ho Chow *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-1894-8903
Ka Kit Chua *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-0446-3155
Jun HuShanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-7954-9965
Zhichao Hu *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0003-1628-4274
Beini Lao *Guangdong Provincial Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.ORCID 0000-0002-0600-143X
Chun Chuen Leung *Hong Zhi Tang Chinese Medicine Clinic, Hong Kong, China (Hong Kong).ORCID 0000-0002-7294-1454
Hong Li *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-6897-9257
Linda Zhong *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-3877-1914
Xusheng Liu *Guangdong Provincial Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.ORCID 0000-0002-0931-4925
Yulong Liu *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0003-3630-6936
Zhenjie Liu *Guangdong Provincial Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.ORCID 0000-0003-1008-0043
Xin Lun *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-2071-4137
Wei Mo *Guang Dong Second Traditional Chinese Medicine Hospital, Guangzhou, China.ORCID 0000-0003-1039-2047
Sheung Yuen Siu *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0003-0234-9348
Zhoujian Xiong *Yongan Hospital, Foshan, China.ORCID 0000-0003-2541-7446
Wing Fai Yeung *School of Nursing, Hong Kong Polytechnic University, Hong Kong, China (Hong Kong).ORCID 0000-0002-7980-3126
Run Yun Zhang *China Academy of Chinese Medical Sciences, Guang An Men Hospital, Beijing, China.ORCID 0000-0003-1803-2249
Xuebin Zhang *School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China (Hong Kong).ORCID 0000-0002-3678-8149
Hong Kong Baptist University · HKGuangdong Provincial Hospital of Traditional Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNChinese University of Hong Kong · HKFoshan Hospital of TCM · CNHong Kong Polytechnic University · HKShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated.

objectiveThe first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme.

methodsAn operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians' (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated.

resultsIntra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34).

conclusionsTaken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.

Indexed as

Medicine, Chinese TraditionalPhotographySmartphoneTelemedicineCoronavirus InfectionsCOVID-19Delphi TechniqueHumansObserver VariationPandemicsPneumonia, ViralReproducibility of ResultsTongue DiseasesCOVID-19Gwet AC2machine learningmobile healthoral diseasesmartphonetelemedicinetongue imagetraditional Chinese medicine

Identifiers

PMID32459647
PMCPMC7380897
OpenAlexW3013347747

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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.