Temporary Tattoos to Simulate Skin Disease: Report and Validation of a Novel Teaching Tool
Bibliographic record
Abstract
PURPOSE: Dermatology is a visual specialty requiring examination and description of skin lesions and the development of analytic skills to establish a diagnosis. Student education in dermatology is challenged by several factors. Although 10% to 15% of a general practitioner's consultations are related to the skin, dermatology is often underrepresented in undergraduate medical curriculums. In addition, more serious lesions, such as malignant melanoma (MM), are promptly biopsied and may not be available for students' examination. The authors carried out this study to learn whether a novel educational tool, a temporary tattoo, could successfully simulate an MM. METHOD: Eighty-one dermatologists and 14 dermatology residents participated in this validity study of a tattoo applied to the arm of a standardized patient (SP) to simulate an MM. The study was conducted at the 82nd Annual Canadian Dermatology Association Conference held in June 2007 in Toronto, Canada. RESULTS: A correct diagnosis was made by 93.8% (76/81) of the dermatologists and 90.5% of the participants (86/95) overall. The tattoo was also evaluated as being very realistic on a five-point Likert scale. CONCLUSIONS: The validation of the tattoo shows potential for use in medical education, such as SP visits and examinations. This teaching tool can be used to simulate a variety of skin lesions, providing a way to visually examine a lesion on the skin of an SP, which would enhance the medical student's learning experience.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".