Patients Presenting with Acute Stroke During COVID-19 Pandemic Era: A Multicenter Retrospective Study
Bibliographic record
Abstract
Background Coronavirus disease-2019 (COVID-19) infection originated in China and was very quickly seen in the Middle East North Africa (MENA) region. This study was undertaken to evaluate COVID-19-related cerebrovascular involvement in the MENA+ region. Methods Retrospective observational regional multicenter study aiming to identify acute stroke presentation and functional independency in patients with COVID-19 infection in the MENA region. The diagnosis of COVID-19 was established by polymerase chain reaction testing in all patients. The National Institute of Health Stroke Scale (NIHSS) was used to evaluate the severity of stroke symptoms. Functional independency was assisted by a modified Rankin Scale (mRS) at 90 days. Results There were 209 patients including 65 COVID-19-related stroke group (CRSG). The mean age was 62.85 ± 15.94 for (CRSG) and 58.69 ± 14.73 in non-COVID-19 stroke group (NCSG). The most prevalent risk factor for both groups was hypertension 45 (69.2%) and 105 (72.9) respectively. Intravenous thrombolysis therapy was delivered to 6/65 (9.2%) in (CRSG) compared to 11 (7.6) in (NCSG). The mean NIHSS at baseline for the (CRSG) was 12.94 ± 9.46, versus 6.08 ± 4.9 in (NCSG). This was statistically significant ( P < 0.001). Functional outcome at the 90-day measured using mRS was worse in the (CRSG) compared to (NCSG) 3.61 ± 2.53, 2.20 ± 2.60 respectively and this was statistically significant ( P = 0.001). Conclusion In this study from multiple countries from the MENA+ region, we showed that acute stroke in patients with active COVID-19 had more severe symptoms at onset and worse 90 days’ outcomes despite the young age. There were no regional differences noted in severity and outcome in the MENA region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".