Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it