The Intracerebral Haemorrhage in Patients With Dengue Fever: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Dengue virus is a neurotropic virus capable of infecting the supporting cells of the central nervous system. One of the most severe neurological consequences of this infection is intracerebral haemorrhage, which is a leading cause of death worldwide. This study aimed to systematically review and analyse the existing literature on this topic, providing insights into the potential neurological consequences for patients with dengue fever. 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 articles that were designed as cohort studies. A critical appraisal was conducted using the Newcastle–Ottawa Scale (NOS) score. Risk was employed as a measure of pooled effect size based on a random‐effects model. Heterogeneity was assessed using the Q test and the I2 index. This meta‐analysis included 6 studies involving a total of 2861 individuals who directly assessed the risk of intracerebral haemorrhage. The reported risk of intracerebral haemorrhage was 14 cases per 1000 dengue fever patients [0.014 (95% CI: 0.002, 0.026), p = 0.020, I2 = 94.64%]. Notably, prospective studies with low methodological quality indicate a higher risk of intracerebral haemorrhage compared to retrospective studies and those of high quality. Given the high risk of intracerebral haemorrhage in patients with dengue fever, it is essential for physicians to evaluate affected individuals 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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".