Anesthesia Guidelines and Practical Recommendations during Covid-19 Pandemic Based on an Evaluating Guideline of Several Countries
Bibliographic record
Abstract
Background: The outbreak of Covid-19 has seriously challenged the world's health systems, which brought about a growing dissemination of a multitude anesthesia guidelines. Considering the collaboration of international collogues with the purpose of saving patients’ lives and health care workers, the primary purpose of this study is to describe and evaluate the national guidelines released for the management of anesthesia in patients with Covid 19. Methods: The required data were collected through systematic review approach by consulting the national guidelines published in the datasets such as Pub-med, Cochrane Library, Embase, Science Direct, and Up-to-date. This inclusive searching approach was supplemented with the World Federation of Anesthesiologists Information Resources website. Results: We reviewed the guidelines disseminated by Australia, Canada, China, India, Italy, South Africa, South Korea, Taiwan, Iran, the United Kingdom, and the United States. The results revealed that all the above guidelines were often used to limit the spread of infection and to maintain the health of health care providers. Considering the scope and mission of the guidelines, the results also showed that the most comprehensive ones were disseminated by Chinese researchers. The most transparent reporting of sources of information was released by South Africa and the United Kingdom scholars. Conclusion: Evidence-based implications, the national guidelines need to be updated to increase their accuracy, clarity, and enforceability.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".