Reconfiguring the scope and practice of regional anesthesia in a pandemic: the COVID-19 perspective
Bibliographic record
Abstract
The COVID-19 outbreak is on the world. While many countries have imposed general lockdown, emergency services are continuing. Healthcare professionals have been infected with the virulent severe acute respiratory syndrome coronavirus-2 (SARS), which spreads by close contact and aerosols. The anesthesiologist is particularly vulnerable to aerosols while performing intubation and other airway related procedures. Regional anesthesia (RA) minimizes the need for airway manipulation and the risks of cross infection to other patients, and the healthcare personnel. In this context, for prioritizing RA over general anesthesia, wherever possible, a structured algorithmic approach is outlined. The role of percentage saturation of hemoglobin with oxygen (oxygen saturation), blood pressure and early use of point-of-care ultrasound in differential diagnosis and specific management is detailed. The perioperative anesthetic implications of multisystem manifestations of COVID-19, anesthetic management options, the scope of RA and considerations for its safe conduct in operating rooms is described. An outline for safe and rapid training of healthcare personnel, with an Entrustable Professional Activity framework for ascertaining the practice readiness among trained residents for RA in COVID-19, is suggested. These are the authors' experiences gained from the current pandemic and similar SARS, Middle East Respiratory Syndrome and influenza outbreaks in recent past faced by our authors in Singapore, India, Hong Kong and Canada.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".