Emergency Department Visits, Care, and Outcome After Stroke and Myocardial Infarction During the COVID-19 Pandemic Phases
Bibliographic record
Abstract
Background It is not known if initial reductions in hospitalization for stroke and myocardial infarction early during the coronavirus disease–2019 pandemic were followed by subsequent increases. We describe the rates of emergency department visits for stroke and myocardial infarction through the pandemic phases. Methods We used linked administrative data to compare the weekly age- and sex-standardized rates of visits for stroke and myocardial infarction in Ontario, Canada in the first 9 months of 2020 to the mean baseline rates (2015-2019) using rate ratios (RRs) and 95% confidence intervals (CIs). We compared care and outcomes by pandemic phases (pre-pandemic was January-March, lockdown was March-May, early reopening was May-July, and late reopening was July-September). Results We identified 15,682 visits in 2020 for ischemic stroke (59.2%; n = 9279), intracerebral hemorrhage (12.2%; n = 1912), or myocardial infarction (28.6%; n = 4491). The weekly rates for stroke visits in 2020 were lower during the lockdown and early reopening than at baseline (RR 0.76, 95% CI [0.66, 0.87] for the largest weekly decrease). The weekly rates for myocardial infarction visits were lower during the lockdown only (RR 0.61, 95% CI [0.46, 0.77] for the largest weekly decrease), and there was a compensatory increase in visits following reopening. Ischemic stroke 30-day mortality was increased during the lockdown phase (11.5% pre-coronavirus disease; 12.2% during lockdown; 9.2% during early reopening; and 10.6% during late reopening, P = 0.015). Conclusion After an initial reduction in visits for stroke and myocardial infarction, there was a compensatory increase in visits for myocardial infarction. The death rate after ischemic stroke was higher during the lockdown than in other phases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".