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 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".