Development of 3D-printed female genital models to improve consent, education, and medico-legal communication
Bibliographic record
Abstract
WHAT WAS THE EDUCATIONAL CHALLENGE?: Public and professional understanding of female genital anatomy is limited, affecting informed consent, clinical communication, and medico-legal interpretation in cases of sexual violence and female genital mutilation (FGM). Two-dimensional diagrams fail to convey the three-dimensional structure of the vulva, making explanations difficult for patients, students, and legal professionals. WHAT WAS THE SOLUTION?: We developed modular, dismantlable 3D-printed vulva and hymen models in prepubertal and adult versions. They feature detachable anatomical components, hymenal variants, and FGM configurations, using bright, non-anatomical colors to ensure clarity and inclusivity. HOW WAS THE SOLUTION IMPLEMENTED?: The models were used in clinical consultations to support informed consent and explain medico-legal findings; in medical and legal education to illustrate anatomical variability and debunk myths; and in courtrooms to help judges and juries understand forensic evidence. WHAT LESSONS WERE LEARNED THAT ARE RELEVANT FOR A WIDER GLOBAL AUDIENCE?: Anatomical literacy is crucial for patient autonomy and justice. 3D models enable clear, inclusive, and interactive education, countering persistent misconceptions about female anatomy. WHAT ARE THE NEXT STEPS?: Formal evaluation is planned to assess knowledge impact. Wider dissemination across clinical, legal, and public health contexts is underway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".