Recent Advances in the Discovery of Biomarkers for Canine Osteosarcoma
Bibliographic record
Abstract
Canine osteosarcoma (OSA) is an aggressive malignancy that frequently metastasizes to the lung and bone. Not only has there been essentially no improvement in therapeutic outcome over the past 3 decades, but there is also a lack of reliable biomarkers in clinical practice. This makes it difficult to discriminate which patients will most benefit from the standard treatment of amputation and adjuvant chemotherapy. The development of reliable diagnostic biomarkers could aid in the clinical diagnosis of primary OSA and metastasis; while prognostic, and predictive biomarkers could allow clinicians to stratify patients to predict response to treatment and outcome. This review summarizes biomarkers that have been explored in canine OSA to date. The focus is on molecular biomarkers identified in tumor samples as well as emerging biomarkers that have been identified in blood-based (liquid) biopsies, including circulating tumor cells, microRNAs, and extracellular vesicles. Lastly, we propose future directions in biomarker research to ensure they can be incorporated into a clinical setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".