Lung cancer diagnosis by trained dogs†
Bibliographic record
Abstract
OBJECTIVES: Early lung cancer (LC) diagnosis is key to improve prognosis. We explored here the diagnostic performance of a trained dog to discriminate exhaled gas samples obtained from patients with and patients without LC and healthy controls. METHODS: After appropriate training, we exposed the dog (a 3-year-old cross-breed between a Labrador Retriever and a Pitbull) to 390 samples of exhaled gas collected from 113 individuals (85 patients with LC and 28 controls, which included 11 patients without LC and 17 healthy individuals) for a total of 785 times. RESULTS: The trained dog recognized LC in exhaled gas with a sensitivity of 0.95, a specificity of 0.98, a positive predictive value of 0.95 and a negative predictive value of 0.98. The area under the curve of the receiver-operating characteristics curve was 0.971. CONCLUSIONS: This study shows that a well-trained dog can detect the presence of LC in exhaled gas samples with an extremely high accuracy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".