P.012 Spinal durotomy repair simulator for deliberate microsurgical practice: integration into a residency training module
Bibliographic record
Abstract
Background: Deliberate practice is one aspect of gaining competency in surgical skills. We have previously integrated a vascular microsurgery module into our residency training curriculum, and have recently described our experience with constructing patient-specific spine models for simulating lumbar spinal durotomy repair. The goal of this project is to develop the necessary infrastructure to facilitate practice on the spine model during residency. Methods: A 3D-printed plastic lumbar spine model was created from a patient computed tomography scan. L2 was manually laminectomized, and paraspinal tissues were simulated using Polyvinyl Chloride (PVC) Plastisol. Harvested bovine pericardium was sewn into tubular form as a dural substitute. The pericardial tubes were tied at either end and attached to intravenous tubing to create a closed loop water system. Results: We are developing a video tutorial describing how to setup and use the model. Residents will be recorded while performing a 1.5 cm durotomy and repair using a surgical microscope available in our training laboratory (Drake-Hunterian Neurovascular Laboratory, London, Ontario, Canada). Residents are asked to grade the realism of the model using a questionnaire. Metrics of quality are to be determined. Conclusions: Our proposed model is a cost-effective, easy-to-prepare lumbar spinal simulator that facilitates microsurgical practice during neurosurgical residency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".