ReviewVeterinary sciences2025
Clinical Reasoning Uncertainty in Veterinary Medical Encounters with a Clinical Example.
Review in Veterinary sciences, 2025. 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
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
3 citing papers in PubMed.
- Interventions to Reduce Antibiotic Prescribing in Small Animal Veterinary Practice: Evidence and Clinical Impact.Antibiotics (Basel, Switzerland) · 2026Review
- From Persuasion to Partnership: Evaluating the Practicalities, Ethics, and Evidence for Implementing Motivational Interviewing in Veterinary Practice.Animals : an open access journal from MDPI · 2026Review
- Exploring Competency Development Through Simulation-Based Preclinical Training in Veterinary Education.Veterinary sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This narrative review examines the complexities of medical uncertainty in veterinary practice, highlighting its significant implications for clinical reasoning and decision-making. Veterinary professionals face inherent uncertainties due to factors such as biological variability, incomplete knowledge, and the pressures of rapidly evolving practices. The distinction between clinical ambiguity and medical uncertainty is crucial, as it informs the coping strategies employed by veterinarians. While uncertainty is often viewed negatively, it can stimulate curiosity and enhance problem-solving capabilities. This review categorizes uncertainty into aleatoric and epistemic types, offering insights into their origins and impacts on veterinary professionals and client interactions. The dynamic nature of uncertainty influences both immediate clinical encounters and long-term professional development, with varying effects based on individual tolerance levels and situational stakes. Despite the growing body of literature on uncertainty, veterinary education often neglects to address this critical aspect, leading to a gap in metacognitive competencies among practitioners. We have included a case example that offers explicit guidelines on the application of the Five Microskills model of clinical teaching. This model is proposed to assist veterinary professionals in effectively managing uncertainty. To enhance the quality of veterinary care, there is an urgent need to integrate uncertainty management into veterinary curricula and ongoing professional development. By fostering an environment that acknowledges and addresses uncertainty, veterinary professionals can improve their clinical reasoning, strengthen client relationships, and ultimately enhance patient outcomes. This review advocates for the adoption of evidence-based practices and collaborative approaches to navigate the complexities of uncertainty, ensuring high standards of care in veterinary medicine.
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.