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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".