Impact of the COVID-19 Pandemic on Stroke Subtype Presentation in Patients Without COVID-19 Infection
Bibliographic record
Abstract
ABSTRACT: Background & Aims: It is unknown if the COVID-19 pandemic and public health measures had an immediate impact on stroke subtypes and etiologies in patients not infected with COVID-19. We aimed to evaluate if the proportion of non-COVID-19-related stroke subtypes (ischemic vs. hemorrhagic) and etiologies (cardioembolic, atherosclerosis, small vessel disease, and others) during the pandemic’s first wave were different from prepandemic. Methods: For this retrospective cohort study, we included patients without COVID-19 with ischemic or hemorrhagic stroke at two large Canadian stroke centers between March–May 2019 (prepandemic cohort) and March–May 2020 (pandemic cohort). Proportions of stroke subtypes and etiologies were compared between cohorts using chi-square tests. Results: The prepandemic cohort consisted of 234 stroke patients and the pandemic cohort of 207 stroke patients. There were no major differences in baseline characteristics. The proportions of ischemic versus hemorrhagic stroke were similar (ischemic stroke: 77% prepandemic vs. 75% pandemic; hemorrhagic stroke:12% prepandemic vs. 14% pandemic; p > 0.05). There were no differences in etiologies, except for a decreased proportion of ischemic stroke due to atherosclerosis in the pandemic cohort (26% prepandemic vs. 15% pandemic; difference: 10.6%, 95%CI: 1.4-19.7; p = 0.03). Notably, during the pandemic, the cause of ischemic stroke was more often unknown because of incomplete work-up (13.3% prepandemic vs. 28.2% pandemic, difference: 14.9%, 95%-CI: 5.7–24.2; p = <0.01). Conclusions: In this study, the pandemic had no clear effect on stroke subtypes and etiologies suggesting a limited impact of the pandemic on stroke triggers. However, the shift from atherosclerosis toward other causes warrants further exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".