P.102 Simulation-based training for surgical instrument recognition
Bibliographic record
Abstract
Background: Surgical simulation training offers trainees the opportunity to practice surgical skills before entering the operating room. The objectives of this study were to determine the effect of simulation for learning instruments for burr hole surgery and whether this learning is translated to real instrument recognition with retention. Methods: Randomized trials of PGY1 neurosurgery residents and perioperative nurses were conducted, using PeriopSim™ for instrument recognition, as well as real instruments. Group A performed simulation tasks using PeriopSim™ prior to identifying real instruments, whereas Group B identified real instruments prior to performing simulation tasks. Nurses’ recall was assessed at seven days. Results: Sixteen residents and 100 nurses were recruited. All participants showed significant overall improvement in their scores for simulated tasks. Group A demonstrated enhanced accuracy and speed of identifying real instruments compared with Group B (p<0.001). Furthermore, knowledge recall testing at one week demonstrated retained learning, shown by 97% accuracy in instrument identification. Conclusions: Our results demonstrate that recognition of surgical instruments improves with repeated use of the PeriopSim™ platform. Instrument knowledge acquired through simulation training results in improved identification and retained recognition of real instruments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".