↓ Skip to main content

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

Overview of attention for article published in BMC Veterinary Research, November 2017
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
90 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study
Published in
BMC Veterinary Research, November 2017
DOI 10.1186/s12917-017-1249-3
Pubmed ID
Authors

Tariku Jibat Beyene, Amanuel Eshetu, Amina Abdu, Etenesh Wondimu, Ashenafi Feyisa Beyi, Takele Beyene Tufa, Sami Ibrahim, Crawford W. Revie

Abstract

The recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle diseases. We used a modified Delphi protocol to select important diseases and Bayesian algorithms to estimate the related disease probabilities based on various clinical signs being present in Ethiopian cattle. A total of 928 cases were diagnosed during the study period across three regions of Ethiopia, around 70% of which were covered by diseases included in VetAfrica-Ethiopia. Parasitic Gastroenteritis (26%), Blackleg (8.5%), Fasciolosis (8.4%), Pasteurellosis (7.4%), Colibacillosis (6.4%), Lumpy skin disease (5.5%) and CBPP (5.0%) were the most commonly occurring diseases. The highest (84%) and lowest (30%) levels of matching between diagnoses made by student practitioners and VetAfrica-Ethiopia were for Babesiosis and Pasteurellosis, respectively. Multiple-variable logistic regression analysis indicated that the putative disease indicated, the practitioner involved, and the level of confidence associated with the prediction made by VetAfrica-Ethiopia were major determinants of the likelihood that a diagnostic match would be obtained. This pilot study demonstrated that the use of such applications can be a valuable means of assisting less experienced animal health professionals in carrying out disease diagnosis which may lead to increased animal productivity through appropriate treatment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 18%
Researcher 15 17%
Student > Bachelor 6 7%
Student > Doctoral Student 5 6%
Lecturer 5 6%
Other 17 19%
Unknown 26 29%
Readers by discipline Count As %
Medicine and Dentistry 11 12%
Agricultural and Biological Sciences 10 11%
Veterinary Science and Veterinary Medicine 10 11%
Nursing and Health Professions 9 10%
Psychology 5 6%
Other 17 19%
Unknown 28 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 August 2018.
All research outputs
#6,926,850
of 23,007,887 outputs
Outputs from BMC Veterinary Research
#531
of 3,065 outputs
Outputs of similar age
#114,467
of 331,173 outputs
Outputs of similar age from BMC Veterinary Research
#19
of 91 outputs
Altmetric has tracked 23,007,887 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,065 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 82% 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 331,173 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 65% of its contemporaries.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.