Simulation in Anesthesia for Perioperative Neuroscience: Present and Future
Bibliographic record
Abstract
The brain's sensitivity to fluctuations in physiological parameters demands precise control of anesthesia during neurosurgery, which, combined with the complex nature of neurosurgical procedures and potential for adverse outcomes, makes neuroanesthesia challenging. Neuroanesthesiologists, as perioperative physicians, work closely with neurosurgeons, neurologists, neurointensivists, and neuroradiologists to provide care for patients with complex neurological diseases, often dealing with life-threatening conditions such as traumatic brain injuries, brain tumors, cerebral aneurysms, and spinal cord injuries. The use of simulation to practice emergency scenarios may have potential for enhancing competency and skill acquisition amongst neuroanesthesiologists. Simulation models, including high-fidelity manikins, virtual reality, and computer-based simulations, can replicate physiological responses, anatomical structures, and complications associated with neurosurgical procedures. The use of high-fidelity simulation can act as a valuable complement to real-life clinical exposure and training in neuroanesthesia.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".