Effectiveness of Simulation‐Based Training in Aneurysm Diagnosis & Coiling in Cerebral Angiography
Bibliographic record
Abstract
Medical specialties are starting to turn to new technologies, such as computer simulation, in order to complement traditional teaching methods. Simulation of anatomically complex procedures, such as angiography, is becoming more practical, however, computer‐based modules require extensive research to assess their effectiveness. There is increasing support in the literature for simulation‐based training in medicine; we are exploring a novel method for alternating simulation scenarios for cerebral angiography. Eight residents (4 radiology/4 neurosurgery) and 8 anatomy graduate students were trained on the Simbionix™ angiography simulator in order to assess skill acquisition. Participants had 8 test sessions to diagnose a cerebral aneurysm with either consistent or alternating practice cases. Subjects then would have 6 sessions to treat the aneurysms by filling them with coils. We hypothesize that the participants will benefit from both training types, but when encountering a new scenario would benefit of alternating training more, and would ultimately decrease procedure time, x‐ray time, contrast used and spatial errors. Preliminary results show a trend towards speedup, consistent with our hypothesis. These findings would have a strong impact on the implementation of novel training protocols in medicine, specifically in angiography, leading to safer, individualized learning modules.
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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 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".