National Perspectives on the Training of Neurosurgery Residents in Stereotactic Radiosurgery
Bibliographic record
Abstract
BACKGROUND: Despite the critical role played by neurosurgeons in performing radiosurgery, neurosurgery residents in Canada have limited exposure to radiosurgery during their training. A survey of neurosurgery residents and faculty along with radiation oncology faculty was conducted to analyze perspectives regarding incorporating formal radiosurgery training into the neurosurgery residency curriculum Methods: An online survey platform was employed. Descriptive statistics were used to summarize center and respondent characteristics. Categorical variables were compared using odds ratios and corresponding 95% confidence intervals. The chi-squared test was utilized to assess statistical significance. A value of p<0.05 was considered significant Results: The response rate was 31% (119/381); 87% (102/119) of respondents were from the neurosurgical specialty and 13% (17/119) from radiation oncology. Some 46% of residents (18/40) were "very uncomfortable" with radiosurgery techniques, and 57% of faculty (42/73) believed that dedicated radiosurgery training would be beneficial though impractical. No respondents felt that "no training" would be beneficial. A total of 46% of residents (19/41) felt that this training would be beneficial and that time should be taken away from other rotations, if needed, while 58% of faculty (42/73) and 75% (28/41) of residents believed that either 1 or 1-3 months of time dedicated to training in radiosurgery would suffice Conclusions: Canadian neurosurgeons are actively involved in radiosurgery. Despite residents anticipating a greater role for radiosurgery in their future, they are uncomfortable with the practice. With the indications for radiosurgery expanding, this training gap can have serious adverse consequences for patients. Considerations regarding the incorporation and optimal duration of dedicated radiosurgery training into the Canadian neurosurgery residency curriculum are necessary.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".