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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".