Stroke metrics during the first year of the COVID-19 pandemic, a tale of two comprehensive stroke centers
Bibliographic record
Abstract
Although a decrease in stroke admissions during the SARS-CoV-2 pandemic has been observed, detailed analyses of the evolution of stroke metrics during the pandemic are lacking. We analyzed changes in stroke presentation, in-hospital systems-of-care, and treatment time metrics at two representative Comprehensive Stroke Centers (CSCs) during the first year of Coronavirus disease 2019 pandemic. From January 2018 to May 2021, data from stroke presentations to two CSCs were obtained. The study duration was split into: period 0 (prepandemic), period 1 (Wave 1), period 2 (Lull), and period 3 (Wave 2). Acute stroke therapies rates and workflow times were compared among pandemic and prepandemic periods. Analyses were adjusted for age, sex, comorbidities, and pre-morbid care needs. There was a significant decrease in monthly hospital presentations of stroke during Wave 1. Both centers reported declines in reperfusion therapies during Wave 1, slowly catching up but never to pre pandemic numbers, and dropping again in Wave 2. Both CSCs experienced in-hospital workflow delays during Waves 1 and 2, and even during the Lull period. Our results highlight the need for proactive strategies to reduce barriers to workflow and hospital avoidance for stroke patients during crisis periods.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".