Surgical Activity of First-Year Canadian Neurosurgical Residents
Bibliographic record
Abstract
INTRODUCTION: Surgical activity is probably the most important component of surgical training. During the first year of surgical residency, there is an early opportunity for the development of surgical skills, before disparities between the skill sets of residents increase in future years. It is likely that surgical skill is related to operative volumes. There are no published guidelines that quantify the number of surgical cases required to achieve surgical competency. The aim of this study was to describe the current trends in surgical activity in a recent cohort of first-year Canadian neurosurgical trainees. METHODS: This study utilized retrospective database review and survey methodology to describe the current state of surgical training for first-year neurosurgical trainees. A committee of five residents designed this survey in an effort to capture factors that may influence the operative activity of trainees. RESULTS: Nine out of a cohort of 20 first-year Canadian neurosurgical trainees that began training in July of 2008 participated in the study. The median number of cases completed by a resident during the initial three month neurosurgical rotation was 66, within which the trainee was identified as the primary surgeon in 12 cases. Intracranial hemorrhage and cerebrospinal fluid diversion procedures were the most common operations to have the trainee as primary surgeon. CONCLUSION: Based on this pilot study, it appears that the operative activity of Canadian first-year residents is at least equivalent to the residents of other studied training systems with respect to volume and diversity of surgical activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".