Novel 3D Custom-Made Silicone Tumor Model as a Support for Teaching Surgical Oncology Principles
Bibliographic record
Abstract
Alternative laboratory teaching methods are becoming increasingly desirable and effective in medical education environments. While ethical concerns associated with the use of live animals in terminal surgery laboratories have been reduced with cadaveric models, availability, and lack of pathology can limit their ability to adequately convey surgical principles and replicate clinical training. We have developed a three-dimensional (3D) custom-made silicone soft tissue tumor model using 3D-printed molds derived from canine soft tissue sarcoma computed tomography images. This novel teaching model allows users to apply surgical oncology principles and perform basic technical tasks such as incisional biopsy, margin demarcation, marginal and wide surgical excision, and inking of surgical margins. A large cohort of students in addition to a small number of professional veterinarians at different levels of specialty training followed the laboratory guidelines and evaluated the simulated tumor model based on a qualitative survey. All participants were able to successfully complete the practical training. The model also allowed the students to identify and correct technical errors associated with biopsy sampling and margin dissection, and to understand the clinical impacts related to those errors. Face and content validity of the model were assessed using Likert-style questionnaires with overall average instructors' scores of 3.8/5 and 4.6/5, respectively. Content validity assessment of the model by the students approximated instructors' evaluation with an overall average score of 4.4/5. This model development emphasizes the efficacy of alternative non-cadaveric laboratory teaching tools and could become a valuable aid in the veterinary curricula.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".