The use of dogs for the detection of infectious diseases; an emerging diagnostic option
Bibliographic record
Abstract
Accurate and timely diagnosis are important aspects of infection prevention and control as reliable testing for the identification of both symptomatic and asymptomatic infected persons may reduce the spread of infection. Common infectious disease-testing strategies require the collection of specimens through often invasive procedures, e.g., venous blood collection, nasopharyngeal swabs, urethra swab, rectal swab, etc. Besides the invasiveness of these procedures, they also require trained laboratory personnel and specialized laboratories for testing. In addition, the collection, transportation, storage, and analysis of samples is time consuming and also costly. These challenges necessitate the need for alternative strategies which are faster, reliable, and non-invasive for screening of both asymptomatic and symptomatic individuals for diseases. Canines have been shown to have extraordinary olfactory acuity and for a long time, trained dogs (e.g., Labrador retrievers, Golden retrievers, German shepherds, Belgian malinois, and many other mixed breeds) have been used for varying purposes, e.g., in search and rescue to find victims of all sorts of events: avalanches, earthquakes, floods, landslides, plane crashes (Kokocińska-Kusiak et al., 2021). Sniffer dogs have also been used for explosive detection to combat terrorism, stop the flow of illegal narcotics or contraband, detect unreported currency, concealed humans, or smuggled agriculture products. Increasingly, the usefulness of sniffer dogs has been studied for the detection of viral, bacterial, and parasitic infections, as well as non-infectious diseases and disorders such as epilepsy, diabetes, and cancer (McCulloch et al., 2006; Cambau et al., 2020; Hardin et al., 2015; Catala et al., 2019).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".