Impact of the New McGill Undergraduate Medical Curriculum on Medical Students' Diagnostic Accuracy of Common Dermatoses Encountered in Primary Care
Bibliographic record
Abstract
Background The McGill Faculty of Medicine implemented a new undergraduate medical curriculum in 2013 with additional preclinical lectures in dermatology. At the time of writing, no Canadian prospective study has been published on undergraduate dermatology training in the context of a complete curricular renewal. Objectives Our study was designed to determine the impact of increasing preclinical teaching in dermatology on medical students’ diagnostic accuracy and learning retention of common dermatoses encountered in primary care. Methods A standardized questionnaire was administered to the Classes of 2015, 2016, 2017, and 2018 in 6 versions for a total of 6 times over their 4 years of training. Each version featured 10 photographs of common dermatoses encountered in primary care. Students were invited to participate anonymously and on a voluntary basis. Results A small absolute, but statistically significant difference, of 3% was detected in the fourth and final year of training between the old curriculum (average score = 70%, standard deviation = 15%) and the new curriculum (average score = 73%, standard deviation = 15%), P = .03. Furthermore, the Class of 2018’s performance improved year by year over the entire 4 years of the new curriculum. Conclusions Additional preclinical lectures in dermatology do improve medical students’ diagnostic accuracy of common dermatoses encountered in primary care. Furthermore, they do retain their learning throughout the preclinical and clerkship years.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".