Stroke risk, phenotypes, and death in COVID-19
Bibliographic record
Abstract
OBJECTIVES: To investigate the hypothesis that strokes occurring in patients with coronavirus disease 2019 (COVID-19) have distinctive features, we investigated stroke risk, clinical phenotypes, and outcomes in this population. METHODS: We performed a systematic search resulting in 10 studies reporting stroke frequency among patients with COVID-19, which were pooled with 1 unpublished series from Canada. We applied random-effects meta-analyses to estimate the proportion of stroke among COVID-19. We performed an additional systematic search for cases series of stroke in patients with COVID-19 (n = 125), and we pooled these data with 35 unpublished cases from Canada, the United States, and Iran. We analyzed clinical characteristics and in-hospital mortality stratified into age groups (<50, 50-70, >70 years). We applied cluster analyses to identify specific clinical phenotypes and their relationship with death. RESULTS: = 0.003). CONCLUSIONS: Stroke is relatively frequent among patients with COVID-19 and has devastating consequences across all ages. The interplay of older age, comorbid conditions, and severity of COVID-19 respiratory symptoms is associated with an extremely elevated mortality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".