A Simulated Tumor for Teaching Principles of Surgical Oncology for Biopsy and Excision of Skin and Subcutaneous Masses to Veterinary Students
Bibliographic record
Abstract
Tumors of the skin and subcutaneous tissues are commonly encountered in primary care practice. The most common of these tumors are mast cell tumors and soft tissue sarcomas, for which the primary treatment is most often surgical excision. Understanding surgical margins, particularly the deep fascial plane, can be difficult for veterinary students. Current techniques to teach these concepts typically rely on cadaver-based laboratories, which require simulated tumors to improve the realism of the laboratory. Tumors can be difficult to replicate in cadaver laboratories; thus a new technique for a simulated tumor was developed. A gelatin-based simulated tumor was injected into the subcutaneous space in two different sites in canine cadavers. Students then practiced incisional biopsy and wide excision of a subcutaneous mass. Students were able to appropriately perform both techniques using the simulated tumors. When the deep margin was not clean on the wide excision, students were able to understand the error by identifying the simulated tumor, reinforcing the concept of obtaining an appropriate deep fascial plane. In summary, this gelatin-based simulated tumor technique was cost-effective, easy to perform, and effective for the teaching laboratory.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".