Abstract P246: Population-Based Rates of Emergency Department Visits for Stroke in the Different Phases of COVID19 Restriction and Reopening
Bibliographic record
Abstract
Introduction: Reductions in hospital visits for stroke during the COVID19 pandemic have been reported, but few have studied this question on a population-basis and less is understood about the temporal trends after economic and social reopening. We aimed to describe the rate of emergency department visits for acute stroke before and after the declaration of the pandemic, through the different phases of reopening, in the population of Ontario, Canada (14 million people). Methods: Using administrative data linkage, we will show the age- and sex-standardized rate of weekly emergency department visits for ischemic or hemorrhagic stroke between January 1 to September 30, 2020. Pandemic declaration was on March 11, 2020 (week 11) and phase 1 re-opening started on May 19, 2020 (week 20) in Ontario. We will use piecewise regression analysis models to evaluate the changes in rates during the pandemic and after reopening. Results: We identified 5,617 emergency department visits for stroke (53% male, median age 74 IQR [63,83]) between January 1 to May 19, 2020 (week 20). Data beyond May 19, 2020 are not available yet, but these will be presented at the ISC 2021. The proportion of patients treated at a comprehensive stroke center was similar pre- and post-pandemic (41.7% versus 42.7%, standard difference (SD) 0.02), as was the proportion of people admitted to hospital (87.9% versus 86.7%, SD 0.03), treated with intravenous thrombolysis (11.6% versus 11.8%, SD 0.01), or underwent endovascular thrombectomy (5.1% versus 4.8%, SD 0.02). Weekly rates of stroke visits decreased after the pandemic declaration, but they seem to increase after week 16 (Figure 1). We show rates from 2019 for comparison. Conclusions: Population-based rates of emergency department visits for stroke initially decreased after the pandemic compared to pre-pandemic rates. Changes in rates after reopening and the results of the piecewise regression analysis will be presented at the ISC 2021.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".