ArticleVeterinary medicine (Auckland, N.Z.)2017
Canine scent detection of canine cancer: a feasibility study.
Article in Veterinary medicine (Auckland, N.Z.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: A Systematic Review.Integrative cancer therapiesPooled it
- Dogs' ability to detect an inflammatory immune response in cattle via olfaction.Frontiers in veterinary science · 2024Article
- Training with Multiple Structurally Related Odorants Fails to Improve Generalization of Ammonium Nitrate Detection in Domesticated Dogs (Animals : an open access journal from MDPI · 2021Article
- Working Dog Training for the Twenty-First Century.Frontiers in veterinary science · 2021Review
- Methodological Considerations in Canine Olfactory Detection Research.Frontiers in veterinary science · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scent detection prowess of dogs has prompted interest in their ability to detect cancer. The purpose of this study was to determine whether dogs could use olfactory cues to discriminate urine samples collected from dogs that did or did not have urinary tract transitional cell carcinoma (TCC), at a rate greater than chance. Dogs with previous scent training (n=4) were initially trained to distinguish between a single control and a single TCC-positive urine sample. All dogs acquired this task (mean =15±7.9 sessions; 20 trials/session). The next training phase used four additional control urine samples (n=5) while maintaining the one original TCC-positive urine sample. All dogs quickly acquired this task (mean =5.3±1.5 sessions). The last training phase used multiple control (n=4) and TCC-positive (n=6) urine samples to pro-mote categorical training by the dogs. Only one dog was able to correctly distinguish multiple combinations of TCC-positive and control urine samples suggesting that it mastered categorical learning. The final study phase evaluated whether this dog would generalize this behavior to novel urine samples. However, during double-blind tests using two novel TCC-positive and six novel TCC-negative urine samples, this dog did not indicate canine TCC-positive cancer samples more frequently than expected by chance. Our study illustrates the need to consider canine olfactory memory and the use of double-blind methods to avoid erroneous conclusions regarding the ability of dogs to alert on specimens from canine cancer patients. Our results also suggest that sample storage, confounding odors, and other factors need to be considered in the design of future studies that evaluate the detection of canine cancers by scent detection dogs.
Indexed as
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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.