ArticleJournal of feline medicine and surgery2025
Predictors and accuracy of visual weight estimation by veterinary students for cats in trap-neuter-return programs.
Article in Journal of feline medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
ObjectivesThe purpose of this study was to evaluate the accuracy of visual weight estimation by veterinary students for free-roaming cats presented in traps during trap-neuter-return (TNR) events and identify variables that predict the percentage difference between estimated and actual weights.MethodsDuring five TNR events, veterinary students, veterinarians and technicians visually estimated weights for 308 cats. Actual cat weights were measured, and the accuracy of visual estimates was evaluated using the percentage of estimates within 10% (PW10) and 20% (PW20) of actual weight. Predictors of percentage difference were analyzed using mixed-effects linear regression. Dosing accuracy of the induction cocktail was assessed by comparing estimate-derived drug doses to the target range.ResultsStudent estimates achieved a PW20 accuracy of 44%, lower than veterinarians (56%) but higher than technicians (35%). Accuracy within 10% (PW10) was limited across all groups, with students achieving 14% compared with veterinarians at 33%. Dosing based on student estimates fell within the target range for 85% of cases, compared with 95-96% for veterinarians, technicians and consensus estimates. Systematic errors included overestimating lighter cats and underestimating heavier cats, with posture and using kilograms vs pounds also affecting accuracy.Conclusions and relevanceAlthough student estimates were less accurate than those by veterinarians, dosing derived from these estimates was clinically acceptable in most cases. Training on systematic biases and leveraging consensus estimates may improve accuracy. Integrating veterinary student weight estimates into TNR programs is feasible with appropriate safeguards, including training on systematic estimate biases, estimate consensus and post-induction monitoring.
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