The Use of In-house 3D-printed Models in Gynecological Counselling: A Quasi-experimental Study
Bibliographic record
Abstract
Objective: The medical applications of 3D printing have gained recent attention worldwide. This study aimed to explore the potential value of using 3D-printed models for patient counselling in the gynaecology clinical setting. Design: A prospective quasi-experimental study (Pretest-Posttest design). Setting: Outpatient gynaecology clinics at the University Hospital Sharjah. Sample: Women presenting to the outpatient clinic with a gynecologic condition that can be anatomically demonstrated using a 3D model. Methods: We developed 3D-printed models of female pelvic organs representative of normal anatomy and various gynecologic pathologies. Participants’ level of understanding of diagnosis was assessed using a structured questionnaire administered before and after 3D-printed model-assisted counselling. Main outcome measures: The level of patients’ understanding and change in knowledge scores after 3D-printed model-assisted counselling. Results: Of 72 women who were enrolled in the study, 84.7% reported an increase in their level of understanding following the 3D-printed model-assisted counselling compared to the pre-counselling session provided conventionally by their gynecologist. The mean total knowledge score significantly increased following 3D-printed model-assisted counselling compared to conventional counselling (27.8 + 2.5 and 14.86 + 6.3, respectively; p=<0.001). Patients’ level of education and prior awareness of diagnosis significantly influenced the magnitude of change in knowledge scores. Patient satisfaction with 3D models was notably high, with 73.6% (N=53) and 23.6% (N=17) of participants rating it as an excellent or very good counselling approach, respectively. Conclusion: The incorporation of 3D printed models into routine gynecologic counselling appears to be feasible and may provide significant improvement in patient education and satisfaction.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".