Neurosurgical performance between experts and trainees: Evidence from drilling task
Bibliographic record
Abstract
BACKGROUND: Lumbar laminectomy is a common neurosurgery that requires precise manipulation of power drills. We examined the fine movement controls of novice neurosurgeons in drilling tasks and compared its accuracy to expert surgeons' performance. METHODS: Four experts and three novice neurosurgeons performed a lumbar laminectomy on a three-dimensional printed spine model. Scene video and surgeons' eye movements were recorded. Independent sample t-Tests were conducted on the number of jump, total fixation durations, pre-jump fixation durations, post-jump fixation durations and jump distances over novice and expert surgeons. RESULTS: No statistically significant differences were recorded in terms of total fixations and pre-jump fixation durations. However, novices had more jumping events, greater jump distances and longer post-jump fixation durations when compared to expert neurosurgeons. CONCLUSION: Differences in movement accuracy and eye measures were found between expert and novice neurosurgeons during a simulated microscopic lumbar laminectomy. A more comprehensive understanding of surgeon's fine movement control mechanism and eye-hand coordination in microsurgery is essential for us before building an enhanced training protocol for surgical residents in neurosurgery.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".