P.014 Cadaveric avian wing model complements live rat model in microsurgical simulation training for neurosurgical residents: technical aspects
Bibliographic record
Abstract
Background: Training of surgical residents based on the traditional Halstedian model is becoming increasingly scrutinized. The emergence of competency-based training has put pressure on training programs to provide high-fidelity simulation sessions that compliment residents’ training in the operating room. Here we present a novel combination of perfused cadaveric avian wing model in conjunction with live rats for neurosurgical resident training. Methods: The brachial artery of cadaveric duck wing was cannulated and connected to a roller pump. The duck wings remain perfused while residents performed microvascular anastomoses of the brachial and ulnar arteries. This took place prior to live rat modules. Results: The duck wing brachial artery diameter measured 1.5-2.0 mm, similar to the proximal middle cerebral artery in humans. The ulnar artery diameter measured 1.0-1.5 mm, similar to the cortical vessels. 8 interrupted stitches were placed during anastomosis using a 10-0 Nylon suture. Residents who performed the duck wing module felt more comfortable when they moved onto the live rat model with a shallower learning curve. Conclusions: The perfused cadaveric avian wing model provides intermediate to high fidelity simulation that complements the live rat model well. The number of rats needed for neurosurgical simulation training could be reduced via the use of avian wings.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".