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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".