Designing a touchless physical examination for a virtual Objective Structured Clinical Examination
Bibliographic record
Abstract
Purpose: Given the COVID-19 pandemic, many Objective Structured Clinical Examinations (OSCEs) have been adapted to virtual formats without addressing whether physical examination maneuvers can or should be assessed virtually. In response, we developed a novel touchless physical examination station for a virtual OSCE and gathered validity evidence for its use. Methods: A touchless physical examination OSCE station was pilot-tested in a virtual OSCE in which Internal Medicine residents were asked to verbalize their approach to the physical examination, interpret images and videos of findings provided upon request, and make a diagnosis. Differences in performance by training year were explored using ANOVA. In addition, data were analyzed based on a modified approach of Bloom's taxonomy of learning: knowledge, understanding, and synthesis. Results: Sixty-seven residents (PGY1-3) participated in the OSCE. Scores on the pilot station were significantly different between training levels (F=3.936, p=0.024, ηp2=0.11). The pilot station-total correlation (STC) was 0.558, and the item-station correlations (ITC) ranged from 0.115 to 0.571, with the most discriminating items being those that assessed higher orders of learning (understanding and synthesis). Conclusion: This touchless physical examination station was feasible, had acceptable psychometric characteristics, and discriminated between residents at different levels of training.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".