P167: The spot the diagnosis! series: using fine art to teach observation skills and medical concepts on a medical education website
Bibliographic record
Abstract
Introduction: Fine art education increases the quality and quantity of observations that medical students make in both art and clinical reports. However, there are few free and accessible resources that teach art and observational skills to healthcare learners and providers. CanadiEM.org, a medical education blog, developed a new series called Spot the Diagnosis! to address this gap. The goals of the Spot the Diagnosis! series are to: 1) use art to explain medical concepts, 2) tie medical concepts to visual art, 3) hone observational skills, and 4) expose healthcare providers to art. Methods: Each piece of art for the Spot the Diagnosis! Series is selected based upon the author’s art history knowledge, resources found using an online search, and/or suggestions made by other healthcare professionals. The accompanying blog post is researched and written by a medical student in a question-and-answer style and peer-reviewed by another medical student and physician. Posts are uploaded monthly to CanadiEM.org and accessible to anyone with an internet connection. Promotion occurs on site, via email, word-of-mouth, and social media. Viewership is tracked using Google Analytics (GA). A survey for readers is planned to assess who, how, and why readers use the series, but results were not available prior to abstract submission. Results: Six Spot the Diagnosis! posts have been published, each of which begins with the selection of a piece of fine arts that showcases a potential medical diagnosis and a blog post outlining an interpretation of the work informed by observations, historical reports, and medical evidence. Each was published as a blog post on a Saturday and added to a page containing a list of all posts in the broader Arts PRN section on CanadiEM. All contained a single piece of art as the focus, 6 ± 2 (median ± IQR) questions, 638 ± 250 words, and 6 ± 3 references. The answers to questions are hidden under drop-down formatting to allow viewers to arrive at their own answers first. In the first 30 days of publication, each post in the series was viewed 1582 ± 401 times. Conclusion: The Spot the Diagnosis! series is an online educational resource published on CanadiEM.org that aims to improve learners medical knowledge and observational skills by featuring fine arts pieces with relevant question-and-answer style posts. This series fills the gap between art and medicine and has been well received by CanadiEM viewers. We look forward to analyzing responses in our survey to further understand how, why, and who uses this new and innovative resource.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.063 |
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".