COVID‐19 implications for pediatric anesthesia: Lessons learnt and how to prepare for the next pandemic
Bibliographic record
Abstract
COVID-19 is mainly considered an "adult pandemic," but it also has strong implications for children and consequently for pediatric anesthesia. Despite the lethality of SARS-CoV-2 infection being directly correlated with age, children have equally experienced the negative impacts of this pandemic. In fact, the spectrum of COVID-19 symptoms among children ranges from very mild to those resembling adults, but may also present as a multisystemic inflammatory syndrome. Moreover, the vast majority of children might be affected by asymptomatic or pauci-symptomatic infection making them the "perfect" carriers for spreading the disease in the community. Beyond the clinical manifestations of SARS-CoV-2 infection, the COVID-19 pandemic may ultimately have catastrophic health and socioeconomic consequences for children and adolescents, which are yet to be defined. The aim of this narrative review is to highlight how COVID-19 pandemic has affected and changed the pediatric anesthesia practice and which lessons are to be learned in case of a future "pandemic." In particular, the rapid evolution and dissemination of research and clinical findings have forced the scientific community to adapt and alter clinical practice on an unseen and pragmatic manner. Equally, implementation of new platforms, techniques, and devices together with artificial intelligence and large-scale collaborative efforts may present a giant step for mankind. The valuable lessons of this pandemic will ultimately translate into new treatments modalities for various diseases but will also have the potential for safety improvement and better quality of care. However, this pandemic has revealed the vulnerability and deficiencies of our health-care system. If not addressed properly, we may end up with a tsunami of burnout and compassionate fatigue among health-care professionals. Pediatric anesthesia and critical care staff are no exceptions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".