In Reply: Clipping of Intracranial Aneurysms by Neurosurgical Trainees Is Safe and Effective: Statewide Retrospective Review of 614 Consecutive Cases in Queensland, Australia
Bibliographic record
Abstract
To the Editor: We thank Rebchuk and colleagues1 for their correspondence and contribution of their experience in neurovascular training from the perspective of their Canadian institution. The letter highlights the challenge in generalization of our data to other systems including marked differences in several critical contextual factors. We note the differences between these two series in case load (eg, their most commonly clipped aneurysm arose from the anterior communicating artery), case selectivity (86% ‘trainee-led') and differing definitions of primary operator status. They also operate within a system that centralizes specialist neurovascular cases, rather than have that case-load spread among multiple centers. As neurosurgeons, our primary responsibility is to ensure the safety of each patient who is under our care, therefore training the next generation is a secondary responsibility. We are encouraged by proposals for innovative approaches to in vivo training such as the reported ‘4-hand technique’; however, the reporting of safety data to support novel approaches is a prerequisite to their wider uptake. In addition, the tension between service provision and training is a relevant consideration when calling for the centralization of services such as open aneurysm treatment. Although we agree that trainees are likely to benefit from centralization/consolidation of services that are in close geographical proximity, the provision of open cerebrovascular capability across the broad landmasses of Australia and Canada requires that regional/isolated neurosurgical centers retain that skillset. For example, one regional neurosurgical service in Queensland covers a geographical catchment area larger than the State of Texas. In our series, the elective and emergency aneurysm cases treated in regional centers were treated exclusively by senior (attending) neurosurgeons as the balance of priorities in that context favors service provision and the maintenance of the skillset by those senior surgeons.2 This tension is also seen in the setting of privatized health care where patient expectations and competition for patient volume make higher levels of intraoperative participation by trainees challenging to facilitate or, indeed, commercially problematic to promote. In these specific contexts, where trainee exposure is compromised by such practicalities, nontraditional training models may have their greatest impact. We are pleased to have reignited the conversation surrounding in vivo neurovascular training in the neurosurgical literature. We hope the publication of our data, and the experience of Rebchuk and colleagues will encourage other authors to contribute safety and outcome data to help support and refine in vivo training practices in cerebrovascular 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".