Abstract 61: Population Incidence Of Cerebral Venous Thrombosis (CVT) From A Large Canadian Cohort 2001-2017
Bibliographic record
Abstract
Background: Previous estimates of CVT incidence vary widely (3-20 per million per year), with more recent studies reporting a higher incidence. Here we present CVT incidence obtained from a large inclusive cohort from the province of British Columbia (BC), Canada (population 5 million) identified through linkage of provincial administrative data. Methods: A retrospective population-based cohort of CVT patients was constructed using ICD 9/10 codes to capture CVT hospitalizations from 2001-2017 in the BC subset of the Canadian Institute for Health Information’s Discharge Abstract Database. The first instance of CVT was extracted and a one-year look-back period was used to exclude prevalent cases. Linkages were made to outpatient prescription data, cancer and prenatal registries to determine patient risk factors. Results: Five hundred and fifty-four unique CVT cases were identified (mean age 50.9, SD 19.4; 55.4% female) (Table 1). Males were more likely to have cases associated with head trauma (12% male vs 7% female, p =0.03) and head and neck infections (13% vs 6%, p =0.009). Cases were diagnosed in the peripartum setting (within 3 months of delivery) for 11% of females. Overall annual incidence was 8.7 (95% CI 8.0-9.4) per 1 000 000 inhabitants. Annual CVT incidence increased over the study period for both men and women (Figure 1). Conclusions: Annual incidence of CVT diagnosis increased from 2001-2017 in BC Canada, similar to trends reported within the United States. This may be due in part to improved ascertainment with increased use of routine vascular neuroimaging over time. Nearly half of cases were male but were more likely to have CVT in the context of trauma or infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".