Cerebral Venous Sinus Thrombosis in Pediatrics with Closed Head Injury: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract This study aims to systematically review the current literature on pediatric cerebral venous sinus thrombosis (CVST) following closed head injury and to evaluate the clinical management of these patients. Systemic review of the literature was conducted using the following databases: PubMed, Google Scholar, Microsoft Academic, Clinical Trials, Cochrane Library, and Web of Science. All databases were searched from their date of inception to June 2022. Inclusion criteria were applied to identify articles reporting on pediatric patients with CVST following closed head injury. Out of the articles screened, 23 met the inclusion criteria, reporting on 23 pediatric patients with CVST. Falls were the most common cause of traumatic CVST (52.2%), followed by motor vehicle accidents (30.4%). Nausea and vomiting were the most common presenting symptoms (71.4%), and magnetic resonance venogram was the most common diagnostic method (43.5%). Multisinus involvement was noted in 52.2% of cases. Patients with falls from height were more likely to receive conservative management than those with nonfall mechanisms of injury (p < 0.05). Pediatric CVST following closed head injury is a rare condition, with only case reports available in the literature. Prompt diagnosis and early treatment can lead to good survival and neurological outcomes. In severe cases, neurosurgical intervention may be necessary to prevent mortality and severe morbidity. This review highlights the need for further research to establish evidence-based management guidelines for this rare but potentially serious condition in the pediatric population.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".