Education in neuroanesthesia and neurocritical care
Bibliographic record
Abstract
PURPOSE OF REVIEW: We summarize the latest evidence in neuroanesthesia and neurocritical care (NCC) training. In addition, we describe the newer advancements that clinical educators face in these subspecialties. Lastly, we highlight educational approaches that may lead to an enhanced learning experience and development of necessary skills for neurosciences trainees. RECENT FINDINGS: Current neuroanesthesia and NCC training requires acquisition of specific skills for increasing complex surgical cases, specialized neurosurgical practice and new perioperative technologies. Furthermore, there is increasing international interest for standardization and accreditation of neuroanesthesia fellowship programs. Recent evidence has demonstrated that well structured training using high-fidelity simulation improves cognitive and technical skills in acute neurological crises. SUMMARY: High-fidelity simulation in perioperative care of neurosurgical patients can be part of formal neuroanesthesia and NCC curricula, and potentially impact trainees' proficiency. A research agenda is needed to validate the assessment of most effective educational interventions in neurosciences trainees with diverse medical backgrounds. Creative combinations of cost-effective interventions including traditional teaching, specific technical skills workshops, low and high-fidelity simulation deserve to be assessed in future studies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".