Needs assessment for simulation training in neuroendoscopy: a Canadian national survey
Bibliographic record
Abstract
OBJECT: In recent years, dramatic changes in surgical education have increased interest in simulation-based training for complex surgical skills. This is particularly true for endoscopic third ventriculostomy (ETV), given the potential for serious intraoperative errors arising from surgical inexperience. However, prior to simulator development, a thorough assessment of training needs is essential to ensure development of educationally relevant platforms. The purpose of this study was to conduct a national needs assessment addressing specific goals of instruction, to guide development of simulation platforms, training curricula, and assessment metrics for ETV. METHODS: Canadian neurosurgeons performing ETV were invited to participate in a structured online questionnaire regarding the procedural steps for ETV, the frequency and significance of intraoperative errors committed while learning the technique, and simulation training modules of greatest potential educational benefit. Descriptive data analysis was completed for both quantitative and qualitative responses. RESULTS: Thirty-two (55.2%) of 58 surgeons completed the survey. All believed that virtual reality simulation training for ETV would be a valuable addition to clinical training. Selection of ventriculostomy site, navigation within the ventricles, and performance of the ventriculostomy ranked as the most important steps to simulate. Technically inadequate ventriculostomy and inappropriate fenestration site selection were ranked as the most frequent/significant errors. A standard ETV module was thought to be most beneficial for resident training. CONCLUSIONS: To inform the development of a simulation-based training program for ETV, the authors have conducted a national needs assessment. The results provide valuable insight to inform key design elements necessary to construct an educationally relevant device and educational program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".