Current and future usefulness and potential of virtual simulation in improving outcomes and reducing complications in endovascular treatment of unruptured intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Simulation training has been used in the aviation industry and surgical specialties for many years, but integration into neurointerventional practice is lagging behind. OBJECTIVE: To investigate how neurointerventionalists perceive the usefulness and limitations of simulation tools for the treatment of unruptured intracranial aneurysms (UIAs), and to identify simulation applications that were perceived to be most valuable for endovascular UIA treatment. METHODS: A web-based international multidisciplinary survey was conducted among neurointerventionalists. Participants were asked for their perceptions on the usefulness of current simulation tools and the potential impact of future simulation tools in endovascular UIA treatment. They identified simulation applications that could add most value to endovascular UIA treatment and help to specifically reduce endovascular UIA treatment complications. RESULTS: 233 neurointerventionalists from 38 countries completed the survey, most of whom (157/233 (67.4%)) had access to a simulator as a trainee, but only 15.3% used it frequently. Most participants (117/233 (50.2%)) considered currently available simulation tools relatively useful for endovascular UIA treatment, with greater value for trainees than for staff. Simulation of new devices (147/233 (63.1%)) and virtual practice runs in individual patient anatomy (119/233 (51.1%)) were considered most valuable for reducing endovascular UIA treatment complications. CONCLUSION: Although neurointerventionalists perceived currently available simulation tools relatively useful, they did not use them regularly during their training. A priori testing of new devices and practice runs in individual patient anatomy in a virtual environment were thought to have the greatest potential for reducing endovascular UIA treatment complications.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".