Bibliographic record
Abstract
Objective To examine the feasibility of olfactory detection of lung cancer by trained sniffer dogs. Methods Three police dogs, one Labrador Retriever and two Springer Spaniels, went through the same training course on detecting odor markers of cancer and were subsequently used to differentiate 52 lung cancer patients and 30 healthy subjects. The sensitivity(true-positive rate)and specificity(true-negative rate)of the olfactory detection were calculated. The consistency of the sniffing outcomes was also compared. Results The Labrador Retriever had a sensitivity of 88. 46% and a false-positive rate of 16. 60%. The two Springer Spaniels exhibited an equal ability to detect cancer patients with a sensitivity of 86. 54% and a false-positive rate of 20%. There was no statistically significant difference between the three dogs in detecting lung cancer(P =0. 994). Conclusion Olfactory detection of lung cancer by trained sniffer dogs may be used to assist clinical diagnosis andenhance diagnostic efficacy. Key words: Lung neoplasms; Diagnosis, differential; Skilled cancer-sniffing dog
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".