Neuroanesthesia Practice During the COVID-19 Pandemic: Recommendations From Society for Neuroscience in Anesthesiology and Critical Care (SNACC)
Bibliographic record
Abstract
The pandemic of coronavirus disease 2019 (COVID-19) has several implications relevant to neuroanesthesiologists, including neurological manifestations of the disease, impact of anesthesia provision for specific neurosurgical procedures and electroconvulsive therapy, and health care provider wellness. The Society for Neuroscience in Anesthesiology and Critical Care appointed a task force to provide timely, consensus-based expert guidance for neuroanesthesiologists during the COVID-19 pandemic. The aim of this document is to provide a focused overview of COVID-19 disease relevant to neuroanesthesia practice. This consensus statement provides information on the neurological manifestations of COVID-19, advice for neuroanesthesia clinical practice during emergent neurosurgery, interventional radiology (excluding endovascular treatment of acute ischemic stroke), transnasal neurosurgery, awake craniotomy and electroconvulsive therapy, as well as information about health care provider wellness. Institutions and health care providers are encouraged to adapt these recommendations to best suit local needs, considering existing practice standards and resource availability to ensure safety of patients and providers.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".