Development of a 3D-printed testicular cancer model for testicular examination education
Bibliographic record
Abstract
INTRODUCTION: Testicular cancer is the most commonly diagnosed malignancy in young males. Testicular examination is a non-invasive and inexpensive means of detecting testicular cancer at an early stage. In this project, a set of 3D-printed models was developed to facilitate teaching testicular examination and improving understanding of testicular malignancies among patients and medical learners. METHODS: Five scrotum models were designed: a control model with healthy testes, and four models containing a healthy testicle and a testicle with an endophytic mass of varying size. The anatomy, texture, and composition of the 3D-printed models were refined using an iterative process between the design team and urologists. The completed models were assessed by six urologists, two urology nurse practitioners, and 32 medical learners. Participants were asked to inspect and palpate each model, and to provide feedback using a five-point Likert scale. RESULTS: Clinicians reported that the models enabled accurate simulation of a testicular examination involving both healthy and pathologic testes (χ̄=4.3±1.0). They agreed that the models would be useful teaching tools for both medical learners (χ̄=4.8±0.5) and patients (χ̄=4.8±0.7). Following an educational session with the models, medical learners reported improvements in confidence and skill in performing a testicular examination. CONCLUSIONS: 3D-printed models can effectively simulate palpation of both healthy and pathologic testes. The developed models have the potential to be a useful adjunct in teaching testicular examination and in demonstrating abnormal findings that require further investigation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".