Airway recommendations for perioperative patients during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
Abstract Purpose Numerous guideline recommendations for airway and perioperative management during the COVID-19 pandemic have been published. We identified, synthesized, and compared guidelines intended for anesthesiologists. Source Member society websites of the World Federation of Societies of Anesthesiologists and the European Society of Anesthesiologists were searched. Recommendations focused on perioperative airway management of patients with proven or potential COVID-19 disease were included. Accelerated screening was used; data extraction was performed by one reviewer and verified by a second. Data was organized into themes based on perioperative phase of care. Principal Findings Thirty unique sets of recommendations were identified. None reported methods for systematically searching or selecting evidence to be included. Four were updated following initial publication. For induction and airway management, most recommended minimizing personnel and having the most experienced anesthesiologist perform tracheal intubation. Significant congruence was observed amongst recommendations that discussed personal protective equipment. Of those that discussed tracheal intubation methods, most (96%) recommended video laryngoscopy, while discordance existed regarding use of flexible bronchoscopy. Intraoperatively, 23% suggested specific anesthesia techniques and most (63%) recommended a specific operating room for patients with COVID-19. Postoperatively, a minority discussed extubation procedures (33%), or care in the recovery room (40%). Non-technical considerations were discussed in 27% and psychological support for healthcare providers in 10%. Conclusion Recommendations for perioperative airway management of patients with COVID-19 overlap to a large extent. However, we also identified significant differences. This may reflect the absence of a coordinated response towards studying and establishing best-practices in perioperative patients with COVID-19. Registration Open Science Framework ( https://osf.io/a2k4u/ )
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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 | 0.024 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".