The Cerebral Haemorrhage and SARS‐CoV‐2: An Emerging Virus From a Meta‐Analysis Perspective
Bibliographic record
Abstract
The central nervous system is a potential target of the COVID-19 virus, and one of the devastating neurological consequences of this infection is cerebral haemorrhage (ICH). Cerebral haemorrhage is a leading cause of death worldwide. This study aimed to systematically review and analyse the existing literature on this topic and provide insights into the potential neurological consequences of COVID-19. A comprehensive search was conducted across the PubMed, Scopus, Web of Science, and Embase databases to extract relevant published data up to February 2025. This meta-analysis included 11 studies involving a total of 197,060 individuals. Subgroup analyses were performed based on the year of publication, hospital sampling wards, and study design. A critical appraisal was carried out using the Newcastle-Ottawa Scale (NOS) score. Risk was utilised as a measure of pooled effect size based on a random-effects model. In this analysis, we identified 11 articles that directly assessed the risk of cerebral haemorrhage. The reported risk of cerebral haemorrhage was five cases per 10,000 COVID-19 patients [0.005 (95% CI: 0.002-0.009), p < 0.001]. Notably, studies published in 2022 and 2023 indicated a significantly higher risk of cerebral haemorrhage compared to earlier years. COVID-19 patients admitted to the intensive care unit (ICU) faced an increased risk of cerebral haemorrhage compared to those admitted to general wards. Meta-regression analysis revealed a statistically significant association between the risk of cerebral haemorrhage and the type of wards in a hospital [0.0089 (95% CI: 0.0067-0.0112), p < 0.001], as well as the year of publication [0.0004 (95% CI: 0.0003-0.0008), p = 0.048]. Therefore, it is essential to evaluate COVID-19 patients admitted to the ICU in recent years for the potential occurrence of cerebral haemorrhage.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".