Abstract 15568: Profound Increases in Out-of-Hospital Cardiopulmonary Arrest Cases Within Most Metropolitan Municipalities During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: Even though most EMS systems globally experienced dramatic decreases in call volumes during the COVID-19 pandemic, many also reported unexpected increases in out-of-hospital cardiac arrest (OHCA) cases. Hypothesis: The pervasiveness of COVID-19 in metropolitan cities might be associated with a greater number of OHCA cases as compared to prior years. Methods: Large city EMS system medical directors responsible for about one-quarter of the U.S. population and millions of others in Europe/Australia were asked to tabulate the absolute number of OHCA cases to which EMS responded within their jurisdictions during the first 4 months of 2020. Results were compared to average numbers encountered during 2018 and 2019. A priori, considering the large populations (high corresponding OHCA frequencies) and case complexities, a 15% increase/decrease was to be considered highly significant (usual variation + 4-8% within the 28-31 days/month). Results: Of 35 major U.S. cities reporting to date, most had significant increases in OHCA, particularly in April with 71% (25/35) of the cities seeing a very large (>15%) increase. Nine cities (26%), all experiencing a high prevalence of COVID-19, had >50% (1.5-fold) increases. Three cities with highly-publicized inundations of COVID-19, experienced more than twice as many arrests (3.5-fold in the worst case). Meanwhile, 3 other cities with relatively low COVID-19 impact to date, had significantly fewer cases. Still, combining all of the 35 cities studied, OHCA increased from 5,009 (2 prior years’ average) to 8,701 (74% increase) during April (Fig 1). Participating cities outside the U.S. (e.g., Paris, London, Perth) mirrored these findings. Conclusions: To date, most, but not all, metropolitan cities have clearly experienced significant increases in OHCA that appear to parallel the prevalence of COVID-19 in their respective jurisdictions. These observations and available forensic data are now part of a work in progress.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".