Deployment of canine scent detection for the screening of COVID-19 on pillowcases of residents in a long-term care setting – a pilot study
Bibliographic record
Abstract
Background: Long-term care (LTC) residents have been disproportionately affected by COVID-19 with higher morbidity and mortality. The use of canines for the detection of COVID-19 has been successful in controlled laboratory settings. They have the potential to provide rapid, non-invasive screening in congregate living settings. The ability to assess whether laboratory-trained canines can transfer their scent detection skills to the clinical settings has had limited evaluation. Methods: At Vancouver Coastal Health, two canines were previously trained and validated to differentiate COVID-19 positive and negative PCR samples from breath, sweat and gargle clinical samples from scent stands. Subsequently, they were taught to alert from pillowcases. Following successful validation and in collaboration with a local LTC, the canines performed weekly blind screening of residents’ pillowcases during the Omicron wave. Results: A third-party, double-blind validation on pillowcases demonstrated an overall sensitivity of 100%. The specificity was 100% for the first canine and 82.6% for the second canine. Although no clinical cases occurred during the six-week pilot project, the agreement between the two canine teams was 98.4% on room alerts. In addition, the two teams were able to seamlessly transfer their laboratory skill sets to the LTC setting. Conclusion: Third-party evaluation determined that canines could successfully be trained to detect COVID-19 from pillowcases. The canine teams were then able to efficiently shift their skill set from the laboratory into an operational setting. This pilot project supports that the deployment of canine detection in a clinical setting is possible and could be considered in congregate living settings.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".