Histologic Grade Does Not Predict Outcome in Dogs with Appendicular Osteosarcoma Receiving the Standard of Care
Bibliographic record
Abstract
Canine appendicular osteosarcoma is an aggressive bone neoplasm that imposes a short survival time. There are several published histologic grading systems for canine osteosarcoma but no universally accepted system. Location within the skeleton and therapy received are both correlated with survival time, but these factors were not always considered when the prognostic value of published grading systems was determined. Our objective was to compare 2 published histologic grading systems in a population of dogs with appendicular osteosarcoma treated with the standard of care for curative intent. Three evaluators graded 85 tumors using 2 histologic grading systems. The relationships between histologic grade as well as individual histologic features and outcome (survival time and disease-free interval) were evaluated using Kaplan-Meier survival functions and a univariate Cox proportional hazards model. Histologic grade, as assigned by any evaluator, did not correlate with outcome. Increased number of mitotic figures per 3 randomly selected 400× microscope fields, as assessed by 1 evaluator, was correlated with both survival time and disease-free interval; this was the only individual histologic feature that was significantly correlated with outcome for any evaluator. These findings cast doubt on the predictive value of routine histologic grading in dogs with appendicular osteosarcoma who receive amputation followed by adjuvant chemotherapy and highlight the need for better tools to predict outcome in canine appendicular osteosarcoma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".