Characterization of canine metastatic pulmonary nodules on <sup>18</sup>F-fluorodeoxyglucose positron emission tomography/computed tomography.
Bibliographic record
Abstract
Background: F-FDG PET/CT) offers functional imaging capabilities and may improve metastasis detection and cancer staging. However, its role in evaluating pulmonary nodules in dogs is not well understood, and species-specific imaging parameters are lacking. Objective: F-FDG PET/CT, with a focus on associations among nodule size, location, and maximum standardized uptake value (SUVmax). Animals and procedure: F-FDG PET/CT in dogs with aggressive cancer were retrospectively reviewed by a Board-certified radiologist. Descriptive and analytical statistical analyses were done to assess relationships between imaging factors. Results: < 0.001). Nodule location did not significantly affect SUVmax. Furthermore, using a proposed SUVmax cutoff of 2.5 obtained from a human guideline, 34.9% (38/109) of nodules were classified as nonmalignant. Conclusion and clinical relevance: F-FDG PET/CT imaging in canine oncology and highlight the need for a dog-specific SUVmax threshold to improve diagnostic accuracy. We emphasize the need for larger prospective studies to refine interpretation of SUVmax values for metastatic pulmonary nodules in dogs.
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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 | 0.002 | 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 | 0.000 |
| 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".