ArticlePathogens (Basel, Switzerland)2020
Bayesian Analysis of Three Methods for Diagnosis of Cystic Echinococcosis in Sheep.
Article in Pathogens (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Abattoir Surveillance of Suspected Cystic Echinococcosis in Cattle: Insights From Molecular Analysis in a Hypoendemic Area.Veterinary medicine international · 2026Article
- Detection ofJournal of parasitic diseases : official organ of the Indian Society for Parasitology · 2022Article
- Response patterns in adventitial layer of Echinococcus granulosus sensu stricto cysts from naturally infected cattle and sheep.Veterinary research · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Diagnosis of cystic echinococcosis (CE) in sheep is essentially based on necropsy findings. Clinical symptoms can be easily overlooked, while the use of immunological tests is still not recommended for an intra vitam diagnosis. This study assessed the performances of three post-mortem laboratory methods in the diagnosis of ovine CE. In the absence of a single and accurate test as a gold standard, the results of multiple analytical tests can be combined to estimate diagnostic performance based on a Bayesian statistical approach. For this purpose, livers (
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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.