51087 UtahDERM: Implementing a Digital Dermatology Education Platform
Bibliographic record
Abstract
Introduction: Patients often present to clinicians who may not know how to accurately identify and diagnose dermatologic conditions.To address this educational gap, the University of Utah Department of Dermatology and Spencer S. Eccles Health Sciences Library digitized 15,000 photographs of both pediatric and adult dermatologic cases donated by Dr. Leonard Swinyer.This report summarizes the development and challenges of the UtahDERM (Dermatology Education Resources & Modules) project, an open-access and searchable database for displaying a vast repository of clinical images.Methods: Approximately 15,000 slides with no patient identifiers were digitized to be placed online in an image viewer.Dermatologists collaborated in applying clinical characteristics (e.g., body location, distribution, secondary change, Fitzpatrick type) to the images using Google Forms, and reviewed the assigned diagnoses for accuracy.The UtahDERM Editor-in-Chief performs a final review and approves of the diagnoses and characteristics.Website usage data was collected via Google Analytics.Results: UtahDERM's Image Viewer is currently in an alpha release stage, allowing for browsing by diagnosis, filtering by characteristics, and textbook chapters.The website received 13,000 views with 6,800 new users from primarily U.S (5,400), Canada (308), and UK (254) from 2022-2023.Challenges included utilizing Google Forms for large scale data collection, mitigating reviewer discrepancies in clinical characteristics, and identifying willing reviewers.Conclusion/Discussion: UtahDERM's Image Viewer is a promising step in enhancing dermatological education but faces challenges in data collection, slide categorization, and reviewer recruitment.Ongoing refinement is necessary to unlock its full potential, and to provide an organizational framework for future open-source largescale image repositories.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.047 |
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".