P.196 Antiplatelet, anticoagulant, or endovascular treatment for stroke prevention in blunt cerebrovascular injury: Retrospective review, systematic review and meta-analysis
Bibliographic record
Abstract
Background: Ischemic stroke occurs following trauma-related blunt cerebrovascular injury (BCVI) in up to 20% of cases. Preventative treatment includes antiplatelets, anticoagulants, and/or endovascular treatment (ET), but the optimal choice remains unclear. The objective of this study was to compare the ischemic stroke rate between these three treatments. Methods: Following PRISMA guidelines, we queried the OVID Medline, Embase, Web of Science, and Cochrane Library databases from September 2019 to inception to identify studies reporting treatment-stratified outcomes in BCVI patients. Meta-analysis was performed to compare outcomes between the treatment groups, using odds ratios. Retrospective review of our institutional experience with BCVI outcomes was performed and added to the meta-analysis. Results: Analysis of seven comparative studies of antiplatelets (n=334) versus anticoagulation (n=325) found no significant difference in ischemic stroke rate (OR 1.27, 95%CI 0.40-3.99), but a decrease in hemorrhagic complications (OR 0.38, 95%CI 0.15-1.00). Analysis of seven comparative studies of antiplatelets/anticoagulants (n=805) versus ET (n=235) also found no significant difference in stroke rate (OR 0.71, 95%CI 0.35-1.42). Conclusions: Antiplatelets and anticoagulants were similarly effective in reducing ischemic stroke risk in BCVI, but antiplatelets were better tolerated in this trauma population. The addition of endovascular treatment did not further reduce stroke risk compared to antiplatelets or anticoagulants alone.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".