Bibliographic record
Abstract
: The development of technical skills is a major goal of any neurosurgical training program. Residency programs in North America are focused on achieving an adequate level of training to produce technically competent surgeons. The training requirements and educational environments needed to produce expert surgeons are incompletely understood. This review explores the theoretical implications of training technical skills to expertise rather than competency in a complex field such as neurosurgery. First, the terms technical expertise and technical competency are defined. Definitions of these qualities are lacking in all surgical specialties. Second, the assessment of technical skills of neurosurgeons are investigated using an expert performance approach. This approach entails the design of tasks that can capture the level of expertise in a reproducible manner. One method to accomplish this involves the use of novel simulators with validated performance metrics. Third, the training of technical skills using simulation is studied in the optic of developing training curricula that would target the development of expertise rather than simple competency. Such curricula should include objective assessments of technical skills, appropriate feedback, and a distributed schedule of deliberate practice. Implementing a focus on the development of expertise rather than simple competency in surgical performance will lead to innovative developments in the field of neurosurgical education. Novel technologies, such as simulation, will play important roles in the training of future expert surgeons, and focused technical skills curricula with a sound theoretical basis should guide the development of all such programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".