ArticleFrontiers in veterinary science2022
Technology Readiness Levels for vaccine and drug development in animal health: From discovery to life cycle management.
Article in Frontiers in veterinary science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Emerging technologies for monitoring breast cancer response to neoadjuvant chemotherapy: a systematic scoping review.Breast (Edinburgh, Scotland) · 2026Review
- Assessing rare disease understanding: a novel disease readiness level framework.Orphanet journal of rare diseases · 2025Article
- Proof of concept studies in mental health systems research: strategy and steps.International journal of mental health systems · 2025Review
- Design of Epitopes fromACS infectious diseases · 2025Article
- Technology Readiness Level Roadmap for Developing Innovative Herbal Medicinal Products.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Adaptation of the technology readiness levels for impact assessment in implementation sciences: The TRL-IS checklist.Heliyon · 2024Article
- Relationship between Dairy Cow Health and Intensity of Greenhouse Gas Emissions.Animals : an open access journal from MDPI · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Public research and innovation initiatives in animal health aim to deliver key knowledge, services and products that improve the control of animal infectious diseases and animal welfare to deliver on global challenges including public health threats, environmental concerns and food security. The Technology Readiness Level (TRL) is a popular innovation policy instrument to monitor the maturity of upcoming new technologies in publicly funded research projects. However, while general definition of the 9 levels on the TRL-scale enable uniform discussions of technical maturity across different types of technology, these definitions are very generic which hampers concrete interpretation and application. Here, we aligned innovation pipeline stages as used in the animal health industry for the development of new vaccines or drugs with the TRL scale, resulting in TRL for animal health (TRLAH). This more bespoke scale can help to rationally allocate funding for animal health research from basic to applied research, map innovation processes, monitor progress and develop realistic progress expectations across the time span of a research and innovation project. The TRLAH thus become an interesting instrument to enhance the translation of public research results into industrial and societal innovation and foster public-private partnerships in animal health.
Indexed as
Identifiers
What OpenQuestion holds
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.