Interim Guidance for Emergency Medical Services Management of Out-of-Hospital Cardiac Arrest During the COVID-19 Pandemic
Bibliographic record
Abstract
The novel coronavirus disease 2019 (COVID-19) has caused immense adverse health consequences around the world. The pandemic’s potential impact upon out-of-hospital cardiac arrest (OHCA) resuscitation and related training are substantial. Given the public health implications of OHCA, resuscitation in the COVID-19 era must strive to correctly balance the best practices achieved by the links in the chain of survival with the added risks of COVID-19. Importantly, changes that are implemented to address COVID-specific risks are likely to affect resuscitation care for all patients with OHCA given the challenges of accurate and timely assessment of COVID infection in the prehospital setting. Preliminary reports indicate that OHCA care and outcome have been adversely impacted in communities with low and high COVID-19 prevalence. Some of the mortality toll may be attributable directly to COVID-19 infection among patients with OHCA. However, an important contributor appears to be adverse impacts of the pandemic on circumstances and care for OHCA patients without COVID-19, highlighting the far-reaching challenges to resuscitation as systems and society navigate the pandemic.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 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".