Teaching Tip: Simulated Tumors as an Aid to Teaching Principles of Surgical Oncology
Bibliographic record
Abstract
Tumors of the skin and subcutaneous tissues are an important cause of morbidity and mortality in small animals. In many cases, surgical excision is an essential component of successful therapy and it should be performed in a specific way based on the type and grade of the tumor. Students require adequate training to develop both clinical decision-making skills and technical surgical skills, which we believe contribute to optimal clinical results. We have developed an inexpensive and simple technique that aims to replicate a naturally occurring subcutaneous tumor and allows trainees to plan and perform three common surgical procedures: incisional biopsy, marginal excision, and wide surgical excision. Artificial tumors were created by subcutaneous injection of a heated, oil-based solution that adhered to surrounding tissue as it solidified. Simulated masses were successfully created in all specimens. Many students performed the exercises with technical proficiency; however, some technical errors were identified and provided an opportunity to discuss the challenges of and solutions to several of these situations. This exercise may be a valuable addition to the veterinary curriculum, aiding student development of both technical skills and knowledge in the field of surgical oncology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".