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 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 | 0.004 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".