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Distribution of race and training-related musculoskeletal injuries by track and location in Thoroughbred and Arabian racehorses.
Equine Vet J
January 2025
İzmir Şirinyer Hippodrome Equine Hospital, Turkish Jockey Club, İzmir, Turkey.
Background: Musculoskeletal injuries (MSI) are a major concern in the horse racing industry, often leading to career-ending outcomes. Contributing factors include conformation, limb and joint defects, hoof structure, age, and hard track surfaces.
Objectives: This study aimed to evaluate the distribution of MSI in Thoroughbred and Arabian racehorses during racing and training, categorised by breed and track surface.
Off to the races: how the Thoroughbred is helping us understand skeletal muscle.
Physiology (Bethesda)
December 2024
School of Animal Sciences, Virginia Polytechnic Institute & State University, Blacksburg, VA, USA.
Risk factors for fatality in jump racing Thoroughbreds in Great Britain (2010-2023).
Equine Vet J
December 2024
Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, Hertfordshire, UK.
Background: The British horseracing industry is committed to reducing equine fatalities in jump racing. Race-related fatalities are a major welfare concern and threaten the sport's social licence to operate.
Objectives: To describe the risk of, and determine risk factors for, fatality in British jump racing.
A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses.
Sci Rep
November 2024
Equine Centre, Melbourne Veterinary School, The University of Melbourne, 250 Princes Hwy Werribee, Melbourne, VIC, 3030, Australia.
Decreasing speed and stride length over successive races have been shown to be associated with musculoskeletal injury (MSI) in racehorses, demonstrating the potential for early detection of MSI through longitudinal monitoring of changes in stride characteristics. A machine learning (ML) approach for early detection of MSI, enforced rest, and retirement events using this same horse-level, race-level, and stride characteristic data across all race sectionals was investigated. A CatBoost model using features from the two races prior to an event had the highest classification performance (sensitivity score for MSI, enforced rest and retirement equal to 0.
View Article and Find Full Text PDFEstimation of genetic parameters for racing time and ranking in Thoroughbred horses.
J Equine Vet Sci
January 2025
Postgraduate Program in Animal Science, Department of Animal Science, UFPR, Curitiba, Parana 80035-050, Brazil.
This study aimed to estimate genetic parameters for race time in seconds and final ranking, as well as to analyze the genetic trends associated with race time. The study utilized a dataset consisting of 23,290 records of race times and final ranks at distances of 1,000, 1,600, and 2,000 m from 6,213 Thoroughbred horses from the São Paulo Jockey Club. Our model considered the year of the run, animal sex, race class, track conditions, the linear effect of horse weight and age, and the quadratic effect of age as fixed covariates.
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