Impact and prevention of errors in endovascular treatment of unruptured intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Preventing errors and complications in neurointervention is crucial, particularly in the treatment of unruptured intracranial aneurysms (UIAs), where the natural history is generally benign, and the margin of treatment benefit small. We aimed to investigate how neurointerventionalists perceive the importance and frequency of errors and the resulting complications in endovascular UIA treatment, and which steps could be taken to prevent them. METHODS: An international multidisciplinary survey was conducted among neurointerventionalists. Participants provided their demographic characteristics and neurointerventional treatment volume. They were asked about their perceptions on the importance and frequency of different errors in endovascular UIA treatment, and which solutions they thought to be most effective in preventing these errors. RESULTS: Two-hundred-thirty-three neurointerventionalists from 38 countries participated in the survey. Participants identified errors in technical execution as the most common source of complications in endovascular UIA treatment (40.4% thought these errors constituted a relatively or very large proportion of all complication sources), closely followed by errors in decision-making/indication (32.2%) and errors related to management of unexpected events (28.4%). Simulation training was thought to be most effective in reducing technical errors, while cognitive errors were believed to be best minimized by abandoning challenging procedures, more honest discussion of complications and better standardization of procedure steps. CONCLUSION: Neurointerventionalists perceived both technical and cognitive errors to be important sources of complications in endovascular UIA treatment. Simulation training, a cultural change, higher acceptance of bail-out strategies and better standardization of procedures were perceived to be most effective in preventing these.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".