Pathology Resident Evaluation During the Pandemic: Testing and Implementation of a Comprehensive Online Pathology Exam
Bibliographic record
Abstract
Despite global digitization, evaluating pathology trainees by paper exams remains the norm. As new social distancing practices require new ways of administering exams, we assessed the viability of an online format for in-house exams from the resident and examiner perspectives. First, pathology residents participated in a practice exam, while staff who were experienced in creating exams were given an online exam-creation demonstration. Subsequently, residents completed a formal 3-hour online exam comprised of multiple-choice, matching, short answer, and whole slide images in place of the paper exam regularly used to evaluate trainees. The experience of the participants was evaluated by surveys. Eighteen residents completed the practice exam; 67% were receptive to the new format and 94% were in favor of moving to digital exams. Seven staff evaluated the digital format and 6 were in favor of it. For the formal online in-house exam, 20 residents participated and 14 completed the survey. Feedback was generally positive with the most common issue being slow-loading digital slides. Exam scores stratified by postgraduate training years in a statistically significant manner, showing positive correlation with resident training level. The online exam format was preferred over paper exams by trainees, with support from both staff and trainees for a permanent transition. Online exams have clear advantages, but technical issues should be addressed before widespread implementation. Our study demonstrates that online exams are a feasible alternative for trainee assessment, especially in socially distanced environments.
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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 | 0.018 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".