Simulation-based training for burr hole surgery instrument recognition
Bibliographic record
Abstract
BACKGROUND: The use of simulation training in postgraduate medical education is an area of rapidly growing popularity and research. This study was designed to assess the impact of simulation training for instrument knowledge and recognition among neurosurgery residents. METHODS: This was a randomized control trial of first year residents from neurosurgery residency training programs across Canada. Eighteen neurosurgery trainees were recruited to test two simulation-based applications: PeriopSim™ Instrument Trainer and PeriopSim™ for Burr Hole Surgery. The intervention was game-based simulation training for learning neurosurgical instruments and applying this knowledge to identify correct instruments during a simulated burr hole surgery procedure. RESULTS: Participants showed significant overall improvement in total score (p < 0.0005), number of errors (p = 0.019) and time saved (p < 0.0005), over three testing sessions when using the PeriopSim™ Instrument Trainer. Participants demonstrated further performance-trained improvements when using PeriopSim™ Burr Hole Surgery. CONCLUSIONS: Training in the recognition and utilization of simulated surgical instruments by neurosurgery residents improved significantly with repetition when using PeriopSim™ Instrument Trainer and PeriopSim™ for Burr Hole Surgery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".