Development of a novel anatomy education tool for teaching dermatomes, cutaneous nerve maps and surface anatomy concepts: The Dry‐Erase Anatomy Mannequin (DrEAM)
Bibliographic record
Abstract
Dermatomes are areas of skin whose sensory innervation can be traced back to a single spinal nerve. Knowledge of dermatome patterns is fundamental to the study of human surface anatomy and is diagnostically useful for a range of neurologic pathologies and injuries. Dermatomes and other surface anatomy concepts are often visually depicted in lectures and textbooks through illustrations. Such visual depictions are two‐dimensional and typically only show the anterior and posterior surface maps of the body side‐by‐side. However, dermatomes and other surface projections, such as cutaneous nerve maps, are 3D shapes that often pass continuously from anterior to posterior. Thus, learning these concepts from such limited visual depictions can provide perceptual challenges to anatomy students who must eventually apply 2D mental schema onto their 3D patients. Additionally, there is a relative lack of emphasis placed on teaching surface anatomy, both within textbooks and as part of formal anatomy curricula. Various instructional methods and low‐fidelity models have been developed by others attempting to convey surface anatomy concepts including body painting exercises and drawing on cloth‐covered anatomy models, to name a few. Here, we report the development of a new educational tool: The Dry‐Erase Anatomy Mannequin (DrEAM). Our novel low‐fidelity learning tool consists of common retail clothing mannequins coated in a commercially available dry‐erase finish and allows students to mark, erase and remark 3D surface anatomy shapes and contours, such as dermatomes, using dry‐erase markers. We also document our recent deployment of the tool as part of our “Dermatome Day” ‐ an informal, interactive extracurricular anatomy teaching session for undergraduate medical students. Other potential use‐cases for the DrEAM in teaching surface anatomy concepts within a range of health professions curricula are further proposed. Lastly, we discuss the formal evaluation of the DrEAM to determine its efficacy in improving student learning outcomes related to surface anatomy concepts as compared to other traditionally used methods. We hypothesize that its use will promote increased understanding of surface anatomy concepts amongst medical students and lead to their improved performance on related test questions when compared to more traditional teaching methods. This abstract is from the Experimental Biology 2018 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".