ArticleBMC veterinary research2026
Diagnostic gap analysis in animal health through combining public databases.
Article in BMC veterinary research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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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0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
The burden of infectious diseases in animals, particularly in livestock, significantly impacts global food security and economic development. Despite advancements in diagnostic technologies, substantial gaps remain that hinder effective disease control and surveillance. This study presents a new methodology for identifying gaps in animal health diagnosis by integrating data from DISCONTOOLS, the Diagnostics for Animals' List of Animal Health Diagnostics, and the World Animal Health Information System. Our analysis highlights significant diagnostic needs, particularly for high-priority infectious diseases in livestock. The findings aim to guide research and development efforts to improve disease surveillance and control.
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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.