Biomedical Scent Detection Dogs: Would They Pass as a Health Technology?
Bibliographic record
Abstract
Biomedical scent detection dogs identify the scent profiles of diseases, such as cancer, diabetes or pathogenic micro-organisms. What the field of biomedical scent detection has been lacking, however, is the assessment of the method from the point of view of a health technology. All health technologies undergo a thorough evaluation of safety, clinical effectiveness and costs, as well as ethical, social, organizational and legal evaluations in some cases. Passing these regulatory controls is a pre-requisite before a technology is approved for use in decision-making about patient outcomes. Biomedical scent detection has a lot of attractive qualities, such as the sensitivity and specificity of the dogs’ noses, safety and relative cost-effectiveness. But the method also has various challenges, in particular regarding its clinical effectiveness. The most pertinent issues to address before the dogs would pass as a health technology are standardization the training techniques, both intra- and inter-dog reproducibility, and generalization of the detection task to early stages of disease progression. We suggest setting realistic goals in terms of what the dogs can and cannot do and a collaborative approach between clinicians and animal psychophysicists.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".