Feasibility of a Dual Neurosurgeon-Scientist Career in Canada: A Survey Study
Bibliographic record
Abstract
OBJECTIVES: Performing 'good work' in either neurosurgery or neuroscience alone is a challenge. Despite this, a large number of neurosurgeons divide their careers between the two fields, and attempt to excel in both arenas simultaneously. The purpose of this study is to explore perceptions on whether it is possible to do good work in both neurosurgery and research simultaneously, or whether one field suffers at the expense of the other. METHODS: This question was put to practicing neurosurgeons via an electronic survey that was distributed to resident and staff neurosurgeons in Canada. RESULTS: 54 surgeons completed the survey, 32 of whom were current or intended neurosurgeon-scientists. Themes explored through the survey included motives behind the pursuit or absence of research in one's neurosurgical career, the quality and feasibility of a dual career, and alternatives to one individual assuming a dual role. CONCLUSIONS: The opinions obtained revealed that it is possible to do good work in both neurosurgery and neuroscience simultaneously, but in reality it is very difficult to do. Alternatives to this dual career, such as collaboration between clinical neurosurgeons and pure scientists for example, may help bridge the gap between clinical and research arenas.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".