P.074 Management, timing of anticoagulation and outcomes of patients with cerebral venous sinus thrombosis: a single centre chart review
Bibliographic record
Abstract
Background: Cerebral venous sinus thrombosis (CVST) accounts for <1% of all strokes. Our objectives were to describe the clinical features and examine the association between timing of anticoagulation therapy and outcomes in CVST patients. Methods: We conducted a retrospective chart review of patients admitted to Hamilton Health Sciences from 2015 – 2020 with imaging confirmed CVST. Results: We included 96 patients, mean age of 47.9 (SD 18.1). The most common clinical presentation was headache (43.8%). Brain trauma was the most common identified risk factor (15.6%), while 27% of individuals had no identified cause. Most patients (57.3%) received anticoagulation within 24hrs of identified CVST, while 26% had a delay (≥48hrs) and 16.7% were not anti-coagulated. The rationale for delaying or not starting anticoagulation included traumatic brain injury (31.8%), neurosurgical procedure (9.1%), presence of venous infarct and/or haemorrhage (27.1%) and unclear rationale (31.8%). At a median of 8 days, more patients without clear indications for delayed or no anticoagulation were disabled (defined by modified Rankin Scale, mRS, score ≥ 2) or dead (mRS 6), compared to those anti-coagulated in 24hrs (87.5% versus 31.8%; RR 2.75; 95% CI 1.74 – 4.35). Conclusions: Unjustified delay in anticoagulation may result in poorer clinical outcomes in CVST patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".