Enhancing Education to Avoid Complications in Endovascular Treatment of Unruptured Intracranial Aneurysms: A Neurointerventionalist’s Perspective
Bibliographic record
Abstract
It is of utmost importance to avoid errors and subsequent complications when performing neurointerventional procedures, particularly when treating low-risk conditions such as unruptured intracranial aneurysms. We used endovascular treatment of unruptured intracranial aneurysms as an example and took a survey-based approach in which we reached out to 233 neurointerventionalists. They were asked what they think are the most important points staff should teach their trainees to avoid errors and subsequent complications in endovascular treatment of unruptured intracranial aneurysms. One hundred twenty-one respondents (51.9%) provided answers in the form of free text responses, which were thematically clustered in an affinity diagram and summarized in this Practice Perspectives. The article is primarily intended for neurointerventional radiology fellows and junior staff and will hopefully provide them the opportunity to learn from the mistakes of their more experienced colleagues.
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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 | 0.008 | 0.021 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".