Trial reportBMJ global health2022
Scent dogs in detection of COVID-19: triple-blinded randomised trial and operational real-life screening in airport setting.
Trial report in BMJ global health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: A Systematic Review.Integrative cancer therapiesPooled it
- Sniffer dogs performance is stable over time in detecting COVID-19 positive samples and agrees with the rapid antigen test in the field.Scientific reports · 2023Trial
- Trained Scent Dog Detection and GC-MS Analysis of Volatile Organic Compounds from Murine Coronavirus-Infected Cell Cultures.Animals : an open access journal from MDPI · 2026Article
- The Role of Olfaction in Dogs: Evolution, Biology, and Human-Oriented Work.Animals : an open access journal from MDPI · 2026Review
- Companion animal and equine clinical research: a Nordic perspective.Acta veterinaria Scandinavica · 2025Article
- Can dogs sniff out cancer? Exploring the role of medical detection dogs in Hong Kong's early diagnosis strategies.Frontiers in medicine · 2025Article
- Public perception of medical detection dogs and other COVID-19 testing strategies.Frontiers in public health · 2025Article
- Olfactory immunology: the missing piece in airway and CNS defence.Nature reviews. Immunology · 2024Review
- Scent Detection Threshold of Trained Dogs toAnimals : an open access journal from MDPI · 2024Article
- Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19.ERJ open research · 2023Article
- Sensitivity and specificity ofHeliyon · 2023Article
- Collection of triatomines from sylvatic habitats by a Trypanosoma cruzi-infected scent detection dog in Texas, USA.PLoS neglected tropical diseases · 2023Article
- Interactions between Humans and Dogs during the COVID-19 Pandemic: Recent Updates and Future Perspectives.Animals : an open access journal from MDPI · 2023Review
- COVID-19 pandemic and air transportation: Summary of Recent Research, Policy Consideration and Future Research Directions.Transportation research interdisciplinary perspectives · 2022Article
- The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures.Biosensors · 2022Article
- Canine real-time detection of SARS-CoV-2 infections in the context of a mass screening event.BMJ global health · 2022Article
- The dogs learning to sniff out disease.Nature · 2022Article
- Dogs can discriminate between human baseline and psychological stress condition odours.PloS one · 2022Article
- Travel in the Time of COVID: A Review of International Travel Health in a Global Pandemic.Current infectious disease reports · 2022Review
- Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo estimate scent dogs' diagnostic accuracy in identification of people infected with SARS-CoV-2 in comparison with reverse transcriptase polymerase chain reaction (RT-PCR). We conducted a randomised triple-blinded validation trial, and a real-life study at the Helsinki-Vantaa International Airport, Finland.
methodsFour dogs were trained to detect COVID-19 using skin swabs from individuals tested for SARS-CoV-2 by RT-PCR. Our controlled triple-blinded validation study comprised four identical sets of 420 parallel samples (from 114 individuals tested positive and 306 negative by RT-PCR), randomly presented to each dog over seven trial sessions. In a real-life setting the dogs screened skin swabs from 303 incoming passengers all concomitantly examined by nasal swab SARS-CoV-2 RT-PCR. Our main outcomes were variables of diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value) for scent dog identification in comparison with RT-PCR.
resultsOur validation experiments had an overall accuracy of 92% (95% CI 90% to 93%), a sensitivity of 92% (95% CI 89% to 94%) and a specificity of 91% (95% CI 89% to 93%) compared with RT-PCR. For our dogs, trained using the wild-type virus, performance was less accurate for the alpha variant (89% for confirmed wild-type vs 36% for alpha variant, OR 14.0, 95% CI 4.5 to 43.4). In the real-life setting, scent detection and RT-PCR matched 98.7% of the negative swabs. Scant airport prevalence (0.47%) did not allow sensitivity testing; our only SARS-CoV-2 positive swab was not identified (alpha variant). However, ad hoc analysis including predefined positive spike samples showed a total accuracy of 98% (95% CI 97% to 99%).
conclusionsThis large randomised controlled triple-blinded validation study with a precalculated sample size conducted at an international airport showed that trained scent dogs screen airport passenger samples with high accuracy. One of our findings highlights the importance of continuous retraining as new variants emerge. Using scent dogs may present a valuable approach for high-throughput, rapid screening of large numbers of people.
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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.