Unraveling the Consequences of the COVID-19 Pandemic on Out-of-hospital Cardiac Arrest: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Aim: The aim of this systematic review and meta-analysis was to assess the influence of the Coronavirus disease-2019 (COVID-19) pandemic on the incidence, characteristics, and clinical consequences of out-of-hospital cardiac arrest (OHCA). Materials and Methods:We searched PubMed, Embase, Scopus, Web of Science, and Cochrane Library databases up to May 30, 2023 for studies containing comparative data of OHCA patients in COVID-19 and pre-pandemic periods.Results: A total of 35 articles concerning to 34 studies screening based on the inclusion criteria.COVID-19 was associated with higher incidence of OHCA at home compared with the pre-pandemic period (p<0.001),longer emergency medical services arrival time (p<0.001),longer on-scene time (p<0.001),as well as reduction of shockable rhythms (p=0.02).COVID-19 compared with the pre-pandemic period was associated with lower survival to hospital admission (11.2% vs. 19.3%;p<0.001).Survival to hospital discharge (SHD) was 4.8% vs. 12.9%, respectively (p<0.001), while SHD with a good neurological outcome also varied and amounted to 3.6% vs. 5.8%, respectively (p<0.001).Conclusion: COVID-19, compared with the pre-pandemic period, was characterized by a reduced rate of defibrillation rhythms during OHCA, as well as a worse prognosis in terms of both survival to hospital admission, SHD, and SHD good neurological outcome.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".