Recommendations for local-regional anesthesia during the COVID-19 pandemic
Bibliographic record
Abstract
Since the beginning of the COVID-19 pandemic, many questions have come up regarding safe anesthesia management of patients with the disease. Regional anesthesia, whether peripheral nerve or neuraxial, is a safe alternative for managing patients with COVID-19, by choosing modalities that mitigate pulmonary function involvement. Adopting regional anesthesia mitigates adverse effects in the post-operative period and provides safety to patients and teams, as long as there is compliance with individual protection and interpersonal transmission care measures. Respecting contra-indications and judicial use of safety techniques and norms are essential. The present manuscript aims to review the evidence available on regional anesthesia for patients with COVID-19 and offer practical recommendations for safe and efficient performance. Desde o início da pandemia de COVID-19, muitas questões surgiram referentes à segurança do manejo anestésico de pacientes acometidos pela doença. A anestesia regional, seja esta periférica ou neuroaxial, é alternativa segura no manejo do paciente COVID-19, desde que o emprego de modalidades que minimizam o comprometimento da função pulmonar seja escolhido. A adoção desta técnica anestésica minimiza os efeitos adversos no pós-operatório e oferece segurança para o paciente e equipe, desde que sejam respeitados os cuidados com proteção individual e de contágio interpessoal. Respeito às contraindicações e emprego criterioso das técnicas e normas de segurança são fundamentais. Este manuscrito tem por objetivo revisar as evidências disponíveis sobre anestesia regional em pacientes com COVID-19 e oferecer recomendações práticas para sua realização segura e eficiente.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".