Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Reference intervals (RI) are an integral component of laboratory diagnostic testing and clinical decision-making and represent estimated distributions of reference values (RV) from healthy populations of comparable individuals. Because decisions to pursue diagnoses or initiate treatment are often based on values falling outside RI, the collection and analysis of RV should be approached with diligence. This report is a condensation of the ASVCP 2011 consensus guidelines for determination of de novo RI in veterinary species, which mirror the 2008 Clinical Laboratory and Standards Institute (CLSI) recommendations, but with language and examples specific to veterinary species. Newer topics include robust methods for calculating RI from small sample sizes and procedures for outlier detection adapted to data quality. Because collecting sufficient reference samples is challenging, this document also provides recommendations for determining multicenter RI and for transference and validation of RI from other sources (eg, manufacturers). Advice for use and interpretation of subject-based RI is included, as these RI are an alternative to population-based RI when sample size or inter-individual variation is high. Finally, generation of decision limits, which distinguish between populations according to a predefined query (eg, diseased or non-diseased), is described. Adoption of these guidelines by the entire veterinary community will improve communication and dissemination of expected clinical laboratory values in a variety of animal species and will provide a template for publications on RI. This and other reports from the Quality Assurance and Laboratory Standards (QALS) committee are intended to promote quality laboratory practices in laboratories serving both clinical and research veterinarians.

Download full-text PDF

Source
http://dx.doi.org/10.1111/vcp.12006DOI Listing

Publication Analysis

Top Keywords

veterinary species
12
guidelines determination
8
determination novo
8
reference intervals
8
clinical laboratory
8
laboratory standards
8
laboratory
5
asvcp reference
4
reference interval
4
interval guidelines
4

Similar Publications

Globally, and have been associated with human gastroenteritis. More importantly, there are increasing reports of strains that are resistant to commonly used antimicrobials. In Rwanda, the prevalence and the antimicrobial susceptibility profiles of thermophilic strains remain underexplored.

View Article and Find Full Text PDF

Clams are an important country food with cultural, environmental, and health significance for Inuit communities in Nunavut. We analyzed the extent, range, and nature of published research on clams in Nunavut, Canada. We used a systematic and transparent scoping review methodology by applying a search string across three databases to identify potentially relevant articles.

View Article and Find Full Text PDF

Introduction: is a well-recognized etiologic agent of upper respiratory tract disease in tortoises. Although frequently reported in both captive and wild populations across Europe, its occurrence in Portugal had not been previously documented. This study aimed to investigate the presence of in apparently healthy captive tortoises in mainland Portugal and to evaluate potential host- and management-related factors associated with infection.

View Article and Find Full Text PDF

Background And Aim: The global shift toward antibiotic-free poultry production necessitates sustainable alternatives to conventional growth promoters. Hydrolyzable tannins (HTs) from plants have shown antimicrobial, antioxidant, and gut-modulatory effects, making them promising feed additives. However, reliance on imported tannins from temperate species limits access for tropical producers, especially in Thailand.

View Article and Find Full Text PDF

Background And Aim: Granulosa cells (GCs) are crucial mediators of follicular development and oocyte competence in goats, with their gene expression profiles serving as potential biomarkers of fertility. However, the lack of a standardized, quantifiable method to assess GC quality using transcriptomic data has limited the translation of such findings into reproductive applications. This study aimed to develop a hybrid deep learning model integrating one-dimensional convolutional neural networks (1DCNNs) and gated recurrent units (GRUs) to classify GCs as fertility-supporting (FS) or non-fertility-supporting (NFS) using single-cell RNA sequencing (scRNA-seq) data.

View Article and Find Full Text PDF