MANAGEMENT OF PATIENTS DIAGNOSED OR SUSPECTED WITH COVID-19 IN CARDIORESPIRATORY ARREST: A SCOPING REVIEW
Bibliographic record
Abstract
ABSTRACT Objective: to map the production of knowledge about the recommendations that can be applied in managing patients diagnosed or suspected with COVID-19 in cardiorespiratory arrest. Method: a scoping review, according to the Joanna Briggs Institute (2020) guidelines. Search was performed in ten data sources, and two electronic search engines were used; from 2001 to 2020. Results: of the 547 studies found, 14 met the inclusion and exclusion criteria. Most studies were published in 2020 (35.7%), and most studies were conducted in Canada (21.4%). It is observed the use of a systematized care to identify the possible means of care that should be provided to patients who suffer a cardiorespiratory arrest in hospitals, such as the monitoring of suspected cases by assessing the victim’s breathing and pulse and identifying arrhythmias and shockable rhythms quickly. Personal protective equipment must be used to protect against droplets and aerosols and respiratory etiquette. Conclusion: managing patients in cardiorespiratory arrest suspected or diagnosis with COVID-19 requiring cardiopulmonary resuscitation should be performed in isolation areas and with the use of adequate protective equipment. There are gaps in scientific productions so that they address more clearly and instructively management when performing cardiopulmonary resuscitation in patients suspected or diagnosed with COVID-19.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".