From principles to practice: Implementation of entrustable professional activities (EPAs) for surgical pathology residency education in a large academic hospital
Bibliographic record
Abstract
Over the past decade, competency-based medical education (CBME) has gained momentum in the United States to develop trainees into independent and confident physicians by the end of their training. Entrustable professional activities (EPAs) are an established methodology for assessing trainee development through an outcomes-driven rather than a time-based model. While EPAs have been utilized as an assessment tool for CBME in Europe and Canada, their validation and implementation in some medical specialties has occurred more recently in the United States. Pediatrics was the first specialty in the US to conduct a large-scale UME-GME pilot. Pathology Residency EPAs were published in 2018; however, implementation in training programs has been slow. We have piloted EPAs in our residency program's surgical pathology rotation and propose a unique set of 4 surgical pathology EPAs to track trainee preparedness for independent practice.
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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 | 0.049 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".