Assessing simulation‐based interventional angiography training in novices
Bibliographic record
Abstract
Laparoscopic surgical procedures require visual‐spatial coordination in workspaces with restricted views, involving constrained motions. The development of the skills needed for these procedures can be facilitated by 3D simulator‐based training. Surgical training programs are introducing additional requirements of competency‐based simulation in their curricula. Cerebral angiography (CA), however, has lagged behind in this regard, relying strictly on clinical case exposure frequency as a means of assessing proficiency. Simulation in CA training has encountered some roadblocks, possibly due to lack of validation studies that would support more widespread acceptance. The AngioMENTOR visual‐haptic simulator has been regarded as an effective training tool, increasing performance in diagnostic CA. However, this simulator has not been tested thoroughly for training interventional skills in CA. In our current study, neurosurgery and radiology residents will be trained in diagnostic CA, and later assessed on their interventional performance on a variety of cases using AngioMENTOR. The participants’ spatial abilities are used as a baseline measure for comparative assessment of their procedure times, fluoroscopy use, contrast injection, and error correction. We hypothesize that individuals with higher spatial ability would perform the procedure more quickly, using less fluoroscopy and spending less time in incorrect vessels. These findings would complement our initial work on diagnostic CA simulation training, and provide quantitative metrics of performance on this simulator‐based task.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".