Understanding Aneurysm Coiling in Practice: A Delphi Inquiry into Expert Perception
Bibliographic record
Abstract
Introduction and Aims Cerebral aneurysm coiling, a prominent procedure under the neurointerventional angiography umbrella, is a challenging endovascular technique requiring significant hands‐on clinical training. As part of the mandatory angiography fellowship, trainees learn and practice complex skills within the walls of the operating theatre (Angio Suite), guided by expert interventionalists. Unfortunately, training assessment is currently limited to subjective scales and quantity of clinical exposure, rather than tested objective criteria adopted in other medical specialties. In order to advance training in this field, objective methods of assessments need to be created, validated and applied to available training systems. However, a representative schema of performed tasks and events in aneurysm coiling needs to be assembled in preparing new assessment methods. Methods A multiple‐phase Delphi assessment was distributed via Survey Monkey to the Canadian Interventional Neuro Group (CING). A total of 85 expert interventionalists were quired on their perception of the importance, frequency and severity of core steps and errors in cerebral angiography and aneurysm coiling. Participants were provided with opportunities to provide feedback on the accuracy of the steps. Questions containing feedback or discrepancy in outcomes were reformatted and resubmitted to active participants in the next phase for clarification. Once an agreement is achieved across the entire procedural spectrum, results are redistributed to all parties and data collection is completed. Results and Discussion A total of 21 experts responded to the survey, with 13 completing the survey in full. The most important steps pertained to the aneurysm coiling stage, with the most disagreement revolving around techniques in femoral artery puncture and catherization of the aorta. The highest frequency of errors was reported during the advancement of gauge and starter wire at the start of the procedure, and inconsistent flushing of the endovascular space. The lowest frequency of errors was found to be during aneurysm coiling. Severity of possible errors climbed throughout the procedure, with the highest scores seen in microcatheter advancement and coil deployment within the aneurysm. These results identify areas of training that are exposed to the highest level of risk (eg. steps within the aneurysm coiling stage) or highest rate of error (eg. aortic and supra‐aortic catherization). The next phase of the study will reassess divergence of scores through step ranking and account for individual expert comments. The results will be essential in shaping the methodology of an Angio Suite‐based procedural workflow assessment and subsequent development of an objective training and assessment protocol. Support or Funding Information There are no conflicts of interest to disclose. This research was partly funded by the Canadian Institutes of Health Research (CIHR) Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award (CGS‐D). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.066 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".