Abstracts from the CanVECTOR 2021 Annual Conference November 5th, 2021
Bibliographic record
Abstract
Background and Aim:The treatment and course of cerebral venous thrombosis (CVT) associated with traumatic brain injury (TBI) is not well-characterized and is often managed conservatively.We reviewed the experience at a large tertiary Canadian hospital. Methods: Cases of CVT between 2008-2018 were identifiedthrough free text search through hospital radiology reports and ICD-10 discharge codes.Total number of TBIs was verified through the provincial trauma database.We examined demographics and clinical characteristics; a single expert re-reviewed imaging.Results: Of 3285 TBI, 54 had an identified concurrent CVT.Mean age was 48 (SD 17.8); 22% were female.Vascular imaging (CTA/V) was done the day of admission in 84%; delay to CTA/V was a median of 1d (range1-13).Almost all CVT (93%) were associated with skull fracture; all but 4 were ipsilateral to the side of the fracture.Most (67%) had at least one repeat CTV, occurring at a median of 3d (range1-74) after the initial scan.Two had evidence of secondary venous infarction on follow-up imaging.One was anticoagulated with unfractionated heparin with CVT as the indication; 69% received no anticoagulation and the remainder received chemoprophylaxis dosing at some point during their admission.Conclusion: Trauma-associated CVT may have been underascertained in the absence of routine vascular neuroimaging for all TBI.Clinical course was also poorly characterized due to a lack of consistent clinical and neuroimaging follow-up.Routine vascular neuroimaging in the event of skull fracture with a consistent followup pathway will help to better determine optimal management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.399 | 0.166 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".