Coronavirus disease 2019 and pediatric anesthesia
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to provide the latest evidence for delivering safe and effective anesthesia care for pediatric patients with coronavirus disease 2019 (COVID-19) and to highlight continuing gaps in the literature. RECENT FINDINGS: Safe and efficient care of pediatric patients with COVID-19 can be delivered with the proper planning, coordination, supplies, and staff preparation. From the start of the pandemic, pediatric anesthesiologists from around the world contributed important insights and shared experience as to how best to adapt anesthesia care for children with COVID-19 requiring general anesthesia and sedation. Although initial efforts focused on creating safe airway management processes, the role of anesthesiologists as perioperative leaders quickly extended to ensuring well-coordinated management of COVID-19 patients throughout the hospital for procedures, including preprocedure testing, patient transport, operating room setup, and ensuring the safety of staff. Several important areas remain not well studied including, the timing of rescheduling elective procedures following COVID-19 infection, the perioperative implications of re-infection, and future considerations of managing vaccinated children. SUMMARY: Pediatric anesthesia care can be safely delivered to children with COVID-19 and after COVID-19 infection. More attention needs to be focused on the perioperative management of COVID-19 children in recovery requiring anesthesia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".