Incidence of Cerebral Venous Thrombosis: A Population-Based Study, Systematic Review, and Meta-Analysis
Bibliographic record
Abstract
Background: Studying the baseline incidence of cerebral venous thrombosis (CVT) prior to COVID-19 and the limitations of how this has been previously reported in the literature will help improve understanding of this disease and how risks may have changed in the post-COVID era. Methods: We examined CVT incidence using linked administrative data in British Columbia, Canada (population 5.2 million). To contextualize our findings, we also examined CVT incidence in the published literature and searched MEDLINE and EMBASE for article titles and abstracts up to Nov 2, 2021 on CVT incidence in adults. We performed abstract screening and full-text review prior to data extraction and explored associations between CVT incidence and year of study, geographic location, and study quality with meta-analyses and meta-regression. A random-effects restricted maximum likelihood model was used. Publication bias was assessed using the Egger tests and using visual inspection of the funnel plot for symmetry. Results: There were 554 unique CVT cases (mean age 50.9 years, 55.4% women) in British Columbia from 2000 to 2017; overall annual incidence was 8.7 (95%CI‚ 8.0–9.4) per million. Incidence increased over time in men across the entire study period, and from 2011 to 2017 in women. We identified 22 other studies on CVT incidence before 2020 (21/23 total studies included in meta-analysis). Annual incidence overall was 12.1 (95% CI‚ 9.9–14.3) per million with significant between-study heterogeneity (I 2 98.8%, Qp-value<0.001). There were no significant associations on meta-regression between incidence and study year, study quality score, or gross national income per capita of the study country. Visual inspection of the funnel plot and a significant Egger test (z=2.8, P <0.01) suggested possible publication bias. Conclusions: Incidence of CVT in Canadian data increased over time but remained lower than in other population-based studies. Significant heterogeneity exists in the literature, which may be subject to publication bias.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".