Risk of stroke in hospitalized SARS-CoV-2 infected patients
Bibliographic record
Abstract
Background: Stroke is one of the most common and fatal neurologic abnormalities. Multiple risk factors, including smoking, hypertension, diabetes and hyperlipidemia, can lead to a stroke. This study aimed to estimate the stroke short-term risk and its associated factors among SARS-CoV-2 hospitalized cases. Methods: This cross-sectional comparison groups was conducted on SARS-CoV-2 infected patients in two phases: Phase 1: cross sectional study in which the prevalence of stroke could be estimated. Phase 2: grouping among the study population in order to find out different risk factors. All patients underwent medical history taking, full general and neurological examinations, PCR or chest CT scan screening, brain CT scan and Canadian Neurological Scale Results: There was no significant correlation between the stroke different types and risk factors. There was an insignificant correlation between stroke severity and risk factors. There was no statistically significant difference in stroke severity; assessed by Canadian neurological scale; between different stroke types. Conclusion: We detected a low incidence of imaging-confirmed ischemic stroke in hospitalized COVID- 19-infected individuals. Mild, Moderate, and Severe on the Canadian neurological scale were unrelated to the observed results (AIS, ICH, and CVT).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".