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 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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".