Undergraduate Dermatology Medical Education: Results of a Large-Scale Patient Viewing Program
Bibliographic record
Abstract
BACKGROUND: Delivering quality dermatologic instruction to medical students can be difficult; time constraints, limited clinical teachers, and a lack of standardization pose challenges. The literature suggests that many trainees and primary care physicians could benefit from increased clinical dermatology teaching. OBJECTIVE: We sought to deliver and analyze the results of a large-scale patient-viewing undergraduate dermatology education program. METHODS: A total of 250 third-year medical students participated in a 32-station patient-viewing program. Voluntary pre- and posttest surveys were administered to evaluate knowledge and self-perceived abilities in dermatology. The identical tests were composed of 20 multiple-choice and 5 self-perception questions. RESULTS: The response rate for completion of pre- and posttests was 24% (N = 59). Pre- and postknowledge test score means were 69.0% and 93.20%, respectively. Pre- and post-self-perception test score means were 3.95/10 and 7.25/10, respectively. Positive student feedback was received on the patient-viewing educational experience. CONCLUSION: Improvements in knowledge scores and self-assessment scores support the potential integration of structured patient-viewing teaching into undergraduate dermatology medical education curricula.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".