P.085 Hot seat concept in neurosurgical exam simulation adopted by the Comprehensive Clinical Neurosurgery Review
Bibliographic record
Abstract
Background: Neurosurgical education is one of the most exciting topics in contemporary neurosurgery. Passing the final boards is a real challenge. Methods: We conducted a prospective study of 48 candidates who attended the hot-seat sessions during CCN review over three years. Detailed statistical analysis was conducted. Those who attended the Hot seats (Group 1) and those who didn’t (Group 2). The neurosurgery exam simulation was conducted using both MCQ and Oral simulated exams with clinical cases led by world expert faculty in a lecture format for the MCQ and 15-minute mock oral sessions which was video-taped scoring candidates in a standardized fashion for their performance. Results: Group 1 had a better MCQ performance (83 %) compared to group 2 (61 %). Candidates were better in data gathering, differential diagnosis and management. They were worst in simulating surgical techniques and follow-up plans. Geographical characterization showed a big range of intra and inter variability in performances. Interestingly, candidates with excellent MCQ performance had moderate hot seat performance while those with moderate MCQ performance did much better during the hot seat session. Conclusions: Our preliminary results showed that simulation of board exams is a method that will help neurosurgery residents not only pass their board exams, but also achieve the best marks.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".