Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IMPORTANCE: The instruction of dermatology can be challenging due to its large scope, heavy clinical nature, and limited curriculum space. Case-based learning (CBL) is an emerging education paradigm and has no current literature on its use in dermatology. OBJECTIVES: Assess CBL in undergraduate dermatology medical education. METHODS: Case-based learning was implemented in the preclerkship dermatology curriculum at the University of Toronto to 3 student cohorts (totaling 710 students and 93 tutors) between May 2016 and April 2017. We analyzed assignment performance, pre- and post-CBL knowledge test scores, and experience surveys on students and tutors. Surveys were evaluated using aggregate descriptive statistics for quantitative data and thematic data analysis for qualitative data. All assessments were anonymous and voluntary. RESULTS: We received strong positive feedback on the CBL experience, with no score less than 3.8 on a 5-point scale (where 5 indicated strongly agree with a positively phrased question). Thematic data analysis revealed several key themes, including positive comments for a specialist tutor, the use of visual media, and the "mini-cases" style of CBL, while challenges included a lack of motivation. Group assignments scored high, ranging from 88.9% to 99.3%. Tracked pre- and post-CBL knowledge test scores showed a 32% (from 42% to 74%) increase in scores after the CBL experience. Conclusion and Relevance: CBL in dermatology medical education was well received by students and tutors, with high scores in content evaluation and knowledge assessment. Future studies should examine optimal delivery methods and its long-term effects on knowledge retention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it