The impact of the COVID-19 pandemic on bystander CPR and AED rates in Canada
Bibliographic record
Abstract
To evaluate whether the COVID-19 pandemic was associated with changes in bystander CPR and automated external defibrillator (AED) application in Canada. We included adult emergency medical services (EMS)-treated out-of-hospital cardiac arrests (OHCAs) from the Canadian national cardiac arrest registry. The outcomes were bystander CPR and AED application. We fit adjusted piecewise linear segmented logistic regression models to estimate whether the peri-COVID period (February 2020-December 2021), in comparison to pre-COVID (January 2018-January 2020), was associated with a change in the odds of bystander CPR and AED application. We also examined subgroups of private and public only OHCAs. Among the 24,410 OHCAs, the median age was 65 years (IQR 50,77), with 7,822 (32%) females. In the pre-COVID (n=11,271) and peri-COVID (n=13,139) periods, 6,244 (55%) and 7,924 (60%) cases received bystander CPR (+4.9% difference, 95% CI 3.7, 6.2), and 502 (4.5%) and 432 (3.3%) were treated with bystander AEDs (-1.2% difference, 95% CI -1.7, -0.68) respectively. The peri-COVID period was associated with an increased odds of bystander CPR (aOR 1.15; 95% CI 1.03, 1.27) and a decreased odds of bystander AED application (aOR 0.65; 95% CI 0.48, 0.86). This appears to be driven by increases in private-setting bystander CPR (aOR 1.19; 95% CI 1.06, 1.33) and decreases in public-setting AED use (aOR 0.59; 95% CI 0.40, 0.88). The COVID-19 pandemic was associated with an increase in bystander CPR and a decrease in bystander AED application.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".