Ischemic stroke in patients that recover from COVID-19: Comparisons to historical stroke prior to COVID-19 or stroke in patients with active COVID-19 infection
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Understanding the relationship of COVID-19 to stroke is important. We compare characteristics of pre-pandemic historical stroke (Pre-C), cases in acute COVID infection (Active-C) and in patients who have recovered from COVID-19 infection (Post-C). METHODS: We interrogated the Qatar stroke database for all stroke admissions between Jan 2019 and Feb 2020 (Pre-C) to Active-C (Feb2020-Feb2021) and Post-C to determine how COVID-19 affected ischemic stroke sub-types, clinical course, and outcomes prior to, during and post-pandemic peak. We used the modified Rankin Scale (mRS) to measure outcome at 90-days (mRS 0-2 good recovery and mRS 3-6 as poor recovery). For the current analysis, we compared the clinical features and prognosis in patients with confirmed acute ischemic stroke. RESULTS: There were 1413 cases admitted (pre-pandemic: 1324, stroke in COVID-19: 46 and recovered COVID-19 stroke: 43). Patients with Active-C were significantly younger, had more severe symptoms, fever on presentation, more ICU admissions and poor stroke recovery at discharge when compared to Pre-C and Post-C. Large vessel disease and cardioembolic disease was significantly more frequent in Active-C compared to PRE-C or post-C. CONCLUSIONS: Stroke in Post-C has characteristics similar to Pre-C with no evidence of lasting effects of the virus on the short-term. However, Active-C is a more serious disease and tends to be more severe and have a poor prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".