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SEED: the six excesses (Liu Yin) evaluation and diagnosis scale

Overview of attention for article published in Chinese Medicine, October 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)

Mentioned by

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3 tweeters
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1 Facebook page

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17 Mendeley
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Title
SEED: the six excesses (Liu Yin) evaluation and diagnosis scale
Published in
Chinese Medicine, October 2015
DOI 10.1186/s13020-015-0059-4
Pubmed ID
Authors

Pei-Jung Chiang, Tsai-Chung Li, Chih-Hung Chang, Li-Li Chen, Jun-Dai Lin, Yi-Chang Su

Abstract

Infections such as common colds, influenza, acute upper respiratory infections, bacterial gastroenteritis, and urinary tract infections are usually diagnosed according to patients' signs and symptoms. This study aims to develop a scale for the diagnosis of infectious diseases based on the six excesses (Liu Yin) etiological theory of Chinese medicine (CM) by the Delphi method. A total of 200 CM-guided diagnostic items measuring signs and symptoms for infectious diseases were compiled from CM literature archives from the Han to Ming dynasties, CM textbooks in both China and Taiwan, and journal articles from the China Knowledge Resource Integrated Database. The items were based on infections and the six excesses (Liu Yin) etiological theory, i.e., Feng Xie (wind excess), Han Xie (coldness excess), Shu Xie (summer heat excess), Shi Xie (dampness excess), Zao Xie (dryness excess), and Huo Xie (fire excess). The items were further classified into the six excess syndromes and reviewed via a Delphi process to reach consensus among CM experts. In total, 178 items with a mean or median rating of 7 or above on a scale of 1-9 from a panel of 32 experts were retained. The numbers of diagnostic items in the categories of Feng (wind), Han (coldness), Shu (summer heat), Shi (dampness), Zao (dryness), and Huo (fire) syndromes were 15, 22, 25, 37, 17, and 62, respectively. A CM-based six excesses (Liu Yin) evaluation and diagnosis (SEED) scale was developed for the evaluation and diagnosis of infectious diseases based only on signs and symptoms.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 18%
Professor 3 18%
Student > Master 2 12%
Student > Bachelor 1 6%
Researcher 1 6%
Other 0 0%
Unknown 7 41%
Readers by discipline Count As %
Medicine and Dentistry 3 18%
Nursing and Health Professions 2 12%
Psychology 2 12%
Decision Sciences 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 0 0%
Unknown 8 47%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 November 2015.
All research outputs
#4,426,363
of 9,727,557 outputs
Outputs from Chinese Medicine
#70
of 257 outputs
Outputs of similar age
#91,617
of 249,862 outputs
Outputs of similar age from Chinese Medicine
#5
of 7 outputs
Altmetric has tracked 9,727,557 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 257 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 249,862 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.