Inter-observer variance of examiner scoring in urology Objective Structured Clinical Examinations (OSCEs)
Bibliographic record
Abstract
INTRODUCTION: The Objective Structured Clinical Examination (OSCE) is an attractive tool of competency assessment in a high-stakes summative exam. An advantage of the OSCE is the ability to assess more realistic context, content, and procedures. Each year, the Queen's Urology Exam Skills Training (QUEST) is attended by graduating Canadian urology residents to simulate their upcoming board exams. The exam consists of a written component and an OSCE. The aim of this study was to determine the inter-observer consistency of scoring between two examiners of an OSCE for a given candidate. METHODS: Thirty-nine participants in 2020 and 37 participants in 2021 completed four stations of OSCEs virtually over the Zoom platform. Each candidate was examined and scored independently by two different faculty urologists in a blinded fashion at each station. The OSCE scoring consisted of a checklist rating scale for each question. An intra-class correlation (ICC) analysis was conducted to determine the inter-rater reliability of the two examiners for each of the four OSCE stations in both the 2020 and 2021 OSCEs. RESULTS: For the 2020 data, the prostate cancer station scores were most strongly correlated (ICC 0.746, 95% confidence interval [CI] 0.556-0.862, p<0.001). This was followed by the general urology station (ICC 0.688, 95% CI 0.464-0.829, p<0.001), the urinary incontinence station (ICC 0.638, 95% CI 0.403-0.794, p<0.001), and finally the nephrolithiasis station (ICC 0.472, 95% CI 0.183-0.686, p<0.001). For the 2021 data, the renal cancer station had the highest ICC at 0.866 (95% CI 0.754-0.930, p<0.001). This was followed by the nephrolithiasis station (ICC 0.817, 95% CI 0.673-0.901, p<0.001), the pediatric station (ICC 0.809, 95% CI 0.660-0.897, p<0.001), and finally the andrology station (ICC 0.804, 95% CI 0.649-0.895, p<0.001). A Pearson correlation coefficient was calculated for all stations, and all show a positive correlation with global exam scores. It is noteworthy that some stations were more predictive of overall performance, but this did not necessarily mean better ICC scores for these stations. CONCLUSIONS: Given a specific clinical scenario in an OSCE exam, inter-rater reliability of scoring can be compromised on occasion. Care should be taken when high-stakes decisions about promotion are made based on OSCEs with limited standardization.
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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 | 0.075 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".