Anesthesia guidelines for COVID-19 patients: a narrative review and appraisal
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has challenged health systems globally and prompted the publication of several guidelines. The experiences of our international colleagues should be utilized to protect patients and healthcare workers. The primary aim of this article is to appraise national guidelines for the perioperative anesthetic management of patients with COVID-19 so that they can be enhanced for the management of any resurgence of the epidemic. PubMed and EMBASE databases were systematically searched for guidelines related to SARS-CoV and SARS-CoV-2. Additionally, the World Federation Society of Anesthesiologists COVID-19 resource webpage was searched for national guidelines; the search was expanded to include countries with a high incidence of SARS-CoV. The guidelines were evaluated using the Appraisal of Guidelines for Research and Evaluation II tool. Guidelines from Australia, Canada, China, India, Italy, South Africa, South Korea, Taiwan, the United Kingdom, and the United States of America were evaluated. All the guidelines focused predominantly on intubation and infection control. The scope and purpose of guidelines from China were the most comprehensive. The UK and South Africa provided the best clarity. Editorial independence, the rigor of development, and applicability scored poorly. Heterogeneity and gaps pertaining to preoperative screening, anesthesia technique, subspecialty anesthesia, and the lack of auditing of guidelines were identified. Evidence supporting the recommendations was weak. Early guidelines for the anesthetic management of COVID-19 patients lacked quality and a robust reporting framework. As new evidence emerges, national guidelines should be updated to enhance rigor, clarity, and applicability.
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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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".