Exploring canine’s olfactive threshold in artificial urine for medical detection
Bibliographic record
Abstract
Canine olfaction is increasingly studied as a tool for detecting cancer and other diseases. Previous pilot studies have demonstrated that dogs can effectively distinguish positive samples from negative samples in humans with breast cancer, achieving sensitivity rates as high as 100%. However, questions remain about dogs' ability to detect low concentrations of volatile organic compounds in complex medium. While dogs' detection thresholds for isoamyl acetate using a mineral oil substrate have been studied, there are no current studies on their detection limits using more complex substrates like urine, relevant in clinical settings. This pilot study aimed to evaluate the olfactory threshold of dogs using artificial urine with various concentrations of isoamyl acetate. Two dogs were trained to detect isoamyl acetate, initially using water as the substrate during the training phase, and subsequently using artificial urine during the testing phase, under single and double-blinded conditions. The dogs were trained to indicate the presence of isoamyl acetate solutions by sitting in front of the positive sample and ignoring controls. Training and testing occurred in a controlled environment, maintaining consistency with the same two handlers, a standardized methodology, and positive reinforcement with toy rewards. Based on double-blind performances, results showed a minimum detection threshold of 6.7 x 10-9 Molar (M) for Nougaro (Springer Spaniel) one dog and 2.1 x 10-7M for Prince (Labrador Retriever). The sample age did not affect performance. However, the position of the cone did, with higher failure rates for the first cone compared to the other three. These findings underscore the potential of trained dogs to detect volatile organic compounds at very low concentrations in complex substrates, supporting their use in clinical diagnostics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".