PSIV-3 Canine olfaction as a disease detection technology: A systematic review
Bibliographic record
Abstract
Abstract Capitalizing on canine olfactory capacity is a promising strategy for detecting and diagnosing human, animal, and plant diseases. The purpose of this review was to assess the extent of current research in canine disease detection. In this systematic review, multiple databases were searched for studies in which dogs were trained to detect diseases or health conditions. Following PRISMA guidelines, 1689 studies were screened and 50 relevant studies identified. The majority of studies (n = 31, 66%) took place in Europe. Lung cancer (n = 11, 22%), prostate cancer (n = 8, 16%), and breast cancer (n = 7, 14%) were the most frequently-studied conditions. Urine (n = 17, 34%) and breath (n = 9, 18%) were the most common sample types. Across all studies, 166 unique detection dogs were tested. The most numerous breed was Labrador Retrievers (n = 24, 14.46%). The median number of dogs per study was 2 (range: 1–20). To analyze experimental design and results, studies including multiple test paradigms were divided into sub-studies (n = 90). In 84.4% of sub-studies (n = 76), dogs were presented with sets of samples and 74.4% (n = 67) reported a constant number of samples per trial. The median number of samples per trial was 7 (range: 2–100). Of the sub-studies reporting a fixed number of positive samples (range: 1–10; n = 55), 87% (n = 48) presented one positive sample per trial. A plurality of sub-studies (n = 44, 49%) presented samples in a lineup. Sensitivity (median: 0.90; range: 0.13 to 1.0; n = 77) and specificity (median: 0.96; range: 0.08 to 1.0; n = 69) were the predominant measures of detection success, although reporting strategies were inconsistent. Dogs appear to have the capacity to detect disease via olfaction; yet the nascent nature of this discipline yields little consistency across studies.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".