An Open Education Physical Model for Teaching Female Pelvic Anatomy
Bibliographic record
Abstract
The anatomy of the pelvic floor is very complex and often a challenge for students to learn. Traditionally, cadavers and plastic models have been utilized as anatomy teaching tools to help students identify structures and visualize the spatial relationships between these structures. However, these types of tools each come with their own shortcomings that limit the student learning experience. Cadavers are expensive and face many ethical and cultural difficulties. The fragility, lack of availability, and lack of uniformity of cadaveric specimens has made them difficult for large groups of students to learn from. Additionally, it is hard to see many pelvic structures because the layers are thin, adherent, and difficult to isolate. Pelvic plastic models on the other hand, often lack a way to show each individual layer without obscuring other structures and are generally far too robust compared to the real pelvis. A novel educational tool was developed in order to better teach female pelvic anatomy: a fabric pelvis dissection model. This model is a combination of a three‐dimensional (3D) printed bony pelvis attached to a wooden board and soft pelvic structures made of textile. The various fabric types and colours distinguish each structure's tissue type. Black stitchwork depict the muscle architecture while white paint depicts the tendinous fibers. Elastic loops, hooks, and button clips allow each structure to be removable, giving the student a unique way to “dissect” the female pelvis while handling the model. Kinesthetic manipulation helps to reduce the high cognitive load placed on the learner while allowing the user to view structures from different angles. This new fabric and 3D‐printed model offers a unique learning experience to the students that would otherwise be hard to obtain. To facilitate mass production of this learning object, the 3D print files and fabric patterns are offered as an open education resource. Support or Funding Information Self‐funded. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".